CN110084714A - Social network influence power maximization approach, device and equipment based on directed tree - Google Patents
Social network influence power maximization approach, device and equipment based on directed tree Download PDFInfo
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Abstract
This application involves a kind of social network influence power maximization approach, device and equipment based on directed tree, comprising: directed tree is constructed according to the digraph of each node；Total weight of expected final live-vertex at the end of each default influence for initially enlivening combination of nodes generation is propagated is calculated according to each default status information for initially enlivening each node in combination of nodes and directed tree；Corresponding preset of maximum total weight is selected initially to enliven initial live-vertex of each node in combination of nodes as directed tree.It is maximized according to selecting the corresponding default each node initially enlivened in combination of nodes of maximum total weight to propagate influence as the initial live-vertex most probable of directed tree, therefore, the technical solution of the application can to a greater degree maximize the informational influence power in social networks.
Description
Technical field
This application involves maximizing influence technical field more particularly to a kind of social network influence power based on directed tree
Maximization approach, device and equipment.
Background technique
With the development of network technology, great development, especially information have also been obtained by the social activity of carrier of network
It propagates, has been similarly obtained great development.
For some information, wider spread scope may require that, it generally can be according to the shadow for influencing propagation model calculate node
Power is rung, so that the node for selecting influence power big is as start node.But in influence process, whether inactive node is by shadow
Sound is existing probability, therefore, is spread in the decision greatest expected that is difficult under normal circumstances for giving the node in any group of.
Summary of the invention
To be overcome the problems, such as present in the relevant technologies at least to a certain extent, the application provides a kind of based on directed tree
Social network influence power maximization approach, device and equipment.
According to a first aspect of the present application, a kind of social network influence power maximization approach based on directed tree is provided, is wrapped
It includes:
Directed tree is constructed according to the digraph of each node；
It is calculated according to each default status information for initially enlivening each node in combination of nodes and the directed tree each default first
Total weight of the influence that the combination of beginning live-vertex generates expected final live-vertex at the end of propagating；
Select the corresponding default each node initially enlivened in combination of nodes of maximum total weight as described oriented
The initial live-vertex of tree.
Optionally, the status information includes node type, enters side neighbours' number；The node type includes leaf section
Point, for the internal node of seed node, the internal node of non-seed node.
Optionally, at most there are two in-degrees for the internal node in the directed tree.
Optionally, described according to each default status information meter for initially enlivening each node in combination of nodes and the directed tree
Calculate total weight of expected final live-vertex at the end of each default influence for initially enlivening combination of nodes generation is propagated, comprising:
When the node v in the directed tree is leaf node and default initially enliven the node in combination of nodes and is more than or equal to
When 1, total weight is calculated according to the first formula；
First formula is
Wherein f (k, v, i) is total weight, and k is the default node initially enlivened in combination of nodes；
When the node v in the directed tree is the internal node for seed node and default is initially enlivened in combination of nodes
Node is more than or equal to 1 and v band, and there are two enter side neighbours u1And u2When, total weight is calculated according to the second formula；
Second formula is
When the node v in the directed tree is the internal node for seed node and default is initially enlivened in combination of nodes
Node is more than or equal to 1 and v and enters side neighbours u with one1When, total weight is calculated according to third formula；
The third formula is
When the node v in the directed tree is the internal node of non-seed node and default is initially enlivened in combination of nodes
Node is more than or equal to 1 and v band, and there are two enter side neighbours u1And u2When, total weight is calculated according to the 4th formula；
4th formula is
When the node v in the directed tree is the internal node of non-seed node and default is initially enlivened in combination of nodes
Node is more than or equal to 1 and v and enters side neighbours u with one1When, total weight is calculated according to the 5th formula；
5th formula is
F (k, v, i)=1+f (k, u1,i+1)。
Optionally, the directed tree is transformed by the digraph of each node according to binary tree property.
According to a second aspect of the present application, a kind of social network influence power maximization device based on directed tree, packet are provided
It includes:
Module is constructed, for constructing directed tree according to the digraph of each node；
Computing module, for according to each default status information for initially enlivening each node in combination of nodes and the directed tree
Calculate total weight of expected final live-vertex at the end of each default influence for initially enlivening combination of nodes generation is propagated；
Selecting module, for selecting the corresponding default each node initially enlivened in combination of nodes of maximum total weight
Initial live-vertex as the directed tree.
Optionally, the status information includes node type, enters side neighbours' number；The node type includes leaf section
Point, for the internal node of seed node, the internal node of non-seed node.
Optionally, at most there are two in-degrees for the internal node in the directed tree.
Optionally, the computing module includes:
First computing unit, for when the node v in the directed tree is leaf node and presets initial live-vertex group
When node in conjunction is more than or equal to 1, total weight is calculated according to the first formula；
First formula is
Wherein f (k, v, i) is total weight, and k is the default node initially enlivened in combination of nodes；
Second computing unit, for when the node v in the directed tree is the internal node for seed node and is preset just
Node in the combination of beginning live-vertex is more than or equal to 1 and v band, and there are two enter side neighbours u1And u2When, total weight is according to second
Formula is calculated；
Second formula is
Third computing unit, for when the node v in the directed tree is the internal node for seed node and is preset just
Node in the combination of beginning live-vertex is more than or equal to 1 and v and enters side neighbours u with one1When, total weight is according to third formula
It is calculated；
The third formula is
4th computing unit, for being the internal node of non-seed node as the node v in the directed tree and presetting just
Node in the combination of beginning live-vertex is more than or equal to 1 and v band, and there are two enter side neighbours u1And u2When, total weight is according to the 4th
Formula is calculated；
4th formula is
5th computing unit, for being the internal node of non-seed node as the node v in the directed tree and presetting just
Node in the combination of beginning live-vertex is more than or equal to 1 and v and enters side neighbours u with one1When, total weight is according to the 5th formula
It is calculated；
5th formula is
F (k, v, i)=1+f (k, u1,i+1)。
According to the third aspect of the application, a kind of social network influence power maximization equipment based on directed tree, packet are provided
It includes:
Processor, and the memory being connected with the processor；
The memory for storing computer program, the computer program be at least used to execute it is as described below based on
The social network influence power maximization approach of directed tree:
Directed tree is constructed according to the digraph of each node；
It is calculated according to each default status information for initially enlivening each node in combination of nodes and the directed tree each default first
Total weight of the influence that the combination of beginning live-vertex generates expected final live-vertex at the end of propagating；
Select the corresponding default each node initially enlivened in combination of nodes of maximum total weight as described oriented
The initial live-vertex of tree.
Optionally, the status information includes node type, enters side neighbours' number；The node type includes leaf section
Point, for the internal node of seed node, the internal node of non-seed node.
Optionally, at most there are two in-degrees for the internal node in the directed tree.
Optionally, described according to each default status information meter for initially enlivening each node in combination of nodes and the directed tree
Calculate total weight of expected final live-vertex at the end of each default influence for initially enlivening combination of nodes generation is propagated, comprising:
When the node v in the directed tree is leaf node and default initially enliven the node in combination of nodes and is more than or equal to
When 1, total weight is calculated according to the first formula；
First formula is
Wherein f (k, v, i) is total weight, and k is the default node initially enlivened in combination of nodes；
When the node v in the directed tree is the internal node for seed node and default is initially enlivened in combination of nodes
Node is more than or equal to 1 and v band, and there are two enter side neighbours u1And u2When, total weight is calculated according to the second formula；
Second formula is
When the node v in the directed tree is the internal node for seed node and default is initially enlivened in combination of nodes
Node is more than or equal to 1 and v and enters side neighbours u with one1When, total weight is calculated according to third formula；
The third formula is
When the node v in the directed tree is the internal node of non-seed node and default is initially enlivened in combination of nodes
Node is more than or equal to 1 and v band, and there are two enter side neighbours u1And u2When, total weight is calculated according to the 4th formula；
4th formula is
When the node v in the directed tree is the internal node of non-seed node and default is initially enlivened in combination of nodes
Node is more than or equal to 1 and v and enters side neighbours u with one1When, total weight is calculated according to the 5th formula；
5th formula is
F (k, v, i)=1+f (k, u1,i+1)。
Optionally, the directed tree is transformed by the digraph of each node according to binary tree property.
The processor is for calling and executing the computer program in the memory.
Technical solution provided by the present application can include the following benefits: is built with first according to the digraph of each node
Then Xiang Shu calculates each default initial live according to each default status information for initially enlivening each node in combination of nodes and directed tree
Total weight of the influence that the combination of nodes that jumps generates expected final live-vertex at the end of propagating, finally selects maximum total weight pair
That answers default initially enlivens initial live-vertex of each node in combination of nodes as the directed tree.The technical side of the application
Case is to have calculated total weight of expected all live-vertexs at the end of influence is propagated, and each preset initial live-vertex
Total weight of all live-vertexs expected from group credit union is one corresponding, total weight is bigger, illustrates to influence actively to save at the end of propagating
Point is more, and spread scope is bigger, therefore, according to select maximum total weight it is corresponding it is default initially enliven it is each in combination of nodes
Node makes to influence to propagate to maximize as the initial live-vertex most probable of the directed tree, therefore, the technical solution of the application
The informational influence power in social networks can be maximized to a greater degree.
It should be understood that above general description and following detailed description be only it is exemplary and explanatory, not
The application can be limited.
Detailed description of the invention
The drawings herein are incorporated into the specification and forms part of this specification, and shows the implementation for meeting the application
Example, and together with specification it is used to explain the principle of the application.
Fig. 1 is to influence to propagate schematic diagram.
Fig. 2 is a kind of social network influence power maximization approach based on directed tree that embodiments herein one provides
Flow diagram.
Fig. 3 is that a kind of social network influence power based on directed tree that embodiments herein two provides maximizes device
Structural schematic diagram.
Fig. 4 is that a kind of social network influence power based on directed tree that embodiments herein three provides maximizes equipment
Structural schematic diagram.
Specific embodiment
Example embodiments are described in detail here, and the example is illustrated in the accompanying drawings.Following description is related to
When attached drawing, unless otherwise indicated, the same numbers in different drawings indicate the same or similar elements.Following exemplary embodiment
Described in embodiment do not represent all embodiments consistent with the application.On the contrary, they be only with it is such as appended
The example of the consistent device and method of some aspects be described in detail in claims, the application.
It influences propagation model and defines the mode and mechanism that influence power is propagated in community network, be that research community network influences
Power is maximumlly basic.And influence to maximize be the research of social network information communication sphere a basic problem, it can be by
It is described as follows: giving a community network G, influence propagation model m and a positive integer k, find the final shadow in community network
The maximum k node of range is rung, so that using this k node as initially enlivening node set, by influence in community network
Propagation, the node number that is finally affected is maximum.
There are two types of states for each node of community network figure in maximizing influence research, are 0 and 1 respectively, wherein 0 is non-
Active state, 1 is active state.After node is changed into active state from an inactive state, which can attempt to influence at its
In the neighbor node of an inactive state.If activated successfully, neighbor node becomes active state from an inactive state.Such as figure
Shown in 1, start node a is active state, has the ability to attempt to influence its adjacent an inactive state node b, c and e.This time-division
For two kinds of situations: a kind of situation is that node a activates node c failed, then node c is still within an inactive state；Another feelings
Condition is that node a activates node b success, therefore node b becomes active state from an inactive state, and node b, which has, at this time influences it
The ability of adjacent node, for example, node b can activate node d.This influence node becomes active state from an inactive state
Process be known as influence propagation.
Currently, it is independent cascade model (IC model) and linear threshold that most commonly used influence propagation model is studied by academia
Model (LT model).
Wherein, linear threshold model is based on Mark Granovetter (mark's Granovetter) research in 1978 week
Enclose the threshold model proposition of the collective behavior of the influence proposition for the personnel for participating in a certain collective activity.
Linear threshold model is that each node ν is assigned with threshold θ (v) ∈ [0,1], the threshold value indicate this node by
To the complexity of influence.The node w adjacent with node v is with non-negative weight bv,wV is had an impact, and all neighbours of v
The b of wv,wThe sum of be less than or equal to 1.The node v of non-active state is in for one, only when its shadow for enlivening neighbor node
It rings the sum of power and is more than or equal to its threshold value, node v can just be activated, i.e., decision individual in network depends on its all neighbor node
Decision.And node v enliven neighbor node can repeatedly participate in activation v.
Its propagation algorithm specifically includes:
(1) initial live-vertex set A.
(2) in t moment, the neighbor node that all being in of node v enlivens state all attempts activation v, if all neighbours
The sum of influence power of live-vertex has been more than the activation threshold of v, then node v is converted to active state at the t+1 moment.
(3) above process constantly repeats, until the sum of influence power of any live-vertex already existing in network not
When can activate the neighbor node for being in inactive node state, communication process terminates.
In addition, independent cascade model is Jacob Goldenberg (Jacob Gordon fort) et al. in the research marketing
When a probabilistic model proposing.The algorithm of the model is: node u attempts to activate the behavior success of its adjacent node v be
One probability is puvEvent, puvIndicate that node u influences the probability of v by side (u, v), and one is in an inactive state
Node is had just enter into the probability of the neighbor node activation of active state independently of the neighbours' for once attempting too drastic node of serving sb. right before
Activity.In addition, arbitrary node u only has an opportunity trial activation its neighbor node v in the prototype network, no matter can be at
Function, at the time of later in, although u itself still keeps active state, it no longer has an influence power, this kind of nodes at
For the live-vertex of no influence power.
Specifically:
(1) initial live-vertex set A.
(2) in t moment, the node u being activated recently has an impact its adjacent node v, and successful probability is puvIf
Multiple neighbor nodes are all the nodes being activated recently, then these nodes will attempt activation node v with random order.
(3) success if node v is activated, at the t+1 moment, node v switchs to active state, will abut to it non-live
Jump node has an impact；Otherwise, node t+1 moment state does not change.
(4) above process constantly repeats, when influential live-vertex is not present in network, communication process knot
Beam.
Since maximizing influence problem is proved to be NP-Hard problem.Bharathi etc. extends IC model and incorporates
Competive factor, gives the approximate algorithm based on special undirected tree, and goal in research is to maximize itself affect power, and refer to
" maximizing influence problem is NPC, we guess that it be for tree is also such " out.
Based on above-mentioned conclusion, academic circles at present can only be preset initially actively when solving the problems, such as maximizing influence from each
The influence power of node is set out, and selects initial live-vertex according to its influence power, can not be by node inactive in communication process by shadow
Loud probability is taken into account, therefore, after selecting initial live-vertex to carry out according only to the influence power for presetting initial live-vertex
Continuous influence, which is propagated, can not largely generate maximum influence spread scope.
And present inventor demonstrates based on tree, i.e., the maximizing influence problem based on directed tree exists
The solution of one polynomial time, and according to the algorithm that the proof procedure proposes propose the application based on directed tree
Social network influence power maximization approach, device and equipment.The technical solution of the application is carried out by way of examples below
It is described in detail.
Embodiment one
Referring to Fig. 2, Fig. 2 be embodiments herein one provide a kind of social network influence power based on directed tree most
The flow diagram of bigization method.
As shown in Fig. 2, the social network influence power maximization approach provided in this embodiment based on directed tree includes:
Step 21 constructs directed tree according to the digraph of each node.
Step 22 calculates each preset according to each default status information for initially enlivening each node in combination of nodes and directed tree
Total weight of final live-vertex is expected at the end of the influence propagation for initially enlivening combination of nodes generation.
Step 23 selects the corresponding default each node initially enlivened in combination of nodes of maximum total weight as directed tree
Initial live-vertex.
Directed tree is constructed according to the digraph of each node first, then according to each default combination of nodes and oriented of initially enlivening
The status information of each node calculates expected final living at the end of each default influence for initially enlivening combination of nodes generation is propagated in tree
Jump total weight of node, finally selects the corresponding default each node initially enlivened in combination of nodes of maximum total weight as having
To the initial live-vertex of tree.The technical solution of the application is to have calculated expected all live-vertexs at the end of influence is propagated
Total weight, and each preset initially enliven combination of nodes can correspond to one expected from all live-vertexs total weight, always
Weight is bigger, illustrates that live-vertex is more at the end of influencing to propagate, spread scope is bigger, therefore, according to the maximum total power of selection
The corresponding default each node initially enlivened in combination of nodes of weight makes to influence to pass as the initial live-vertex most probable of directed tree
Maximization is broadcast, therefore, the technical solution of the application can to a greater degree maximize the informational influence power in social networks.
In step 21, which is the digraph with the direction of propagation changed according to given community network,
In, directed tree is to be transformed by the digraph of each node according to binary tree property, and the internal node in directed tree is up to
Two in-degrees.
Guaranteeing internal node, at most there are two the methods of in-degree can be and increases dummy node in directed tree, and by the void
The weight definition of quasi- node is 0, and the weight definition of non-virtual node is 1.
In step 22, status information includes node type, enters side neighbours' number；Node type includes leaf node, is
The internal node of the internal node of seed node, non-seed node.
In addition, due to LT model and IC+ model be it is of equal value, for LT model, the calculating of total weight specifically can be with
Are as follows:
When the node v in directed tree is leaf node and the default node initially enlivened in combination of nodes is more than or equal to 1,
Total weight is calculated according to the first formula；
First formula is
Wherein f (k, v, i) is total weight, and k is the default node initially enlivened in combination of nodes；
When the node v in directed tree is the internal node for seed node and the default node initially enlivened in combination of nodes
Enter side neighbours u more than or equal to 1 and there are two v bands1And u2When, total weight is calculated according to the second formula；
Second formula is
When the node v in directed tree is the internal node for seed node and the default node initially enlivened in combination of nodes
More than or equal to 1 and v has one and enters side neighbours u1When, total weight is calculated according to third formula；
Third formula is
When the node v in directed tree is the internal node of non-seed node and the default node initially enlivened in combination of nodes
Enter side neighbours u more than or equal to 1 and there are two v bands1And u2When, total weight is calculated according to the 4th formula；
4th formula is
When the node v in directed tree is the internal node of non-seed node and the default node initially enlivened in combination of nodes
More than or equal to 1 and v has one and enters side neighbours u1When, total weight is calculated according to the 5th formula；
5th formula is
F (k, v, i)=1+f (k, u1,i+1)。
Its runing time is O (nk).
Wherein, IC+ model is the deformation of IC model, specifically: in each step, an inactive node only allows receiving one
It is a enter side adjacent active nodes influence.Assuming that v has h to enter side adjacent active nodes u1 ... uh, then following h+1 event is
It is unique.
Adjacent active nodes ui has an impact its adjacent node v, and successful probability isWherein, i=1,
2 ..., h, the probability not activated are
It should be noted that influencing model for certainty, i.e., 100% influences received IC model, herein to the calculating
Process is also illustrated:
Equally, when v is leaf node, total weight can be calculated by the 6th formula, and the 6th formula is
When v is internal node and enters side neighbours u with there are two1And u2When, 1 is more than or equal to for k, total weight can basis
7th formula is calculated, and the 7th formula is
When v is internal node and enters side neighbours u with one1When, 1 is more than or equal to for k, total weight can be according to the 8th
Formula is calculated, and the 8th formula is f (k, v)=w (v)+f (k, u1)。
Above formula gives dynamic programming algorithm, and runing time is O (n ' k), and wherein n ' is the number of nodes in T.
It, can be in terms of national defence it should be noted that the technical solution of the application can be applied in various social networks
Minimum cost is from fast propagation emergency message on these seed nodes to large quantities of crowds.In marketing, it is known that such as how most
It is highly useful that good mode, which spends for our advertisement and obtains maximization,.In practical field such as political election, candidate
Seek effectively to propagate his popularity or political point view in voter.Emergency such as happens suddenly earthquake information needs limited
Time in travel to each resident of community quickly.In social science, we can track the progress or difference of viewpoint
Between group, such as innovation of civilization or business.In epidemiological survey, it is how from individual to individual that we, which can map disease,
It propagates.
Embodiment two
Referring to Fig. 3, Fig. 3 be embodiments herein two provide a kind of social network influence power based on directed tree most
Disguise the structural schematic diagram set greatly.
As shown in figure 3, the social network influence power maximization device provided in this embodiment based on directed tree includes:
Module 31 is constructed, for constructing directed tree according to the digraph of each node；
Computing module 32, by initially being enlivened in combination of nodes and directed tree based on the status information of each node according to each preset
Calculate total weight of expected final live-vertex at the end of each default influence for initially enlivening combination of nodes generation is propagated；
Selecting module 33, for selecting the corresponding default each node initially enlivened in combination of nodes of maximum total weight to make
For the initial live-vertex of directed tree.
Further, status information includes node type, enters side neighbours' number；Node type includes leaf node, is kind
The internal node of the internal node of child node, non-seed node.
In addition, at most there are two in-degrees for the internal node in directed tree.
Specifically, computing module includes:
First computing unit is leaf node for the node v in the directed tree and default initially enlivens in combination of nodes
Node be more than or equal to 1 when, total weight is calculated according to the first formula；
First formula is
Wherein f (k, v, i) is total weight, and k is the default node initially enlivened in combination of nodes；
Second computing unit, for being internal node and the default initial live for seed node as the node v in directed tree
Node in jump combination of nodes is more than or equal to 1 and v band, and there are two enter side neighbours u1And u2When, total weight is carried out according to the second formula
It calculates；
Second formula is
Third computing unit, for being internal node and the default initial live for seed node as the node v in directed tree
Node in jump combination of nodes is more than or equal to 1 and v and enters side neighbours u with one1When, total weight is counted according to third formula
It calculates；
Third formula is
4th computing unit, for being the internal node of non-seed node as the node v in directed tree and presetting initial live
Node in jump combination of nodes is more than or equal to 1 and v band, and there are two enter side neighbours u1And u2When, total weight is carried out according to the 4th formula
It calculates；
4th formula is
5th computing unit, for being the internal node of non-seed node as the node v in directed tree and presetting initial live
Node in jump combination of nodes is more than or equal to 1 and v and enters side neighbours u with one1When, total weight is counted according to the 5th formula
It calculates；
5th formula is
F (k, v, i)=1+f (k, u1,i+1)。
Embodiment three
Referring to Fig. 4, Fig. 4 be embodiments herein three provide a kind of social network influence power based on directed tree most
The structural schematic diagram of bigization equipment.
As shown in figure 4, the social network influence power maximization equipment provided in this embodiment based on directed tree includes:
Processor 41, and the memory 42 being connected with processor；
For storing computer program, computer program is at least used to execute if embodiment one is based on directed tree memory
Social network influence power maximization approach；
Processor is for calling and executing the computer program in memory.
Wherein, which can be the mobile communication intelligents equipment such as mobile phone, plate, be also possible to computer etc. with processor
With the equipment of memory.The algorithm routine that the method research and development based on embodiment one can be installed in above equipment, for difference
Social network-i i-platform, its biggest impact power that can reach is calculated to the simulation of the initial live-vertex of various combination, finally
Find out the live-vertex combination that can reach biggest impact power.According to calculated result of the algorithm routine in above equipment, mention
Height can achieve a possibility that maximizing influence, and the information propagated can be applied to various industries, such as the marketing, political affairs
Control election, emergency notice, defence and military etc..
About the device in above-described embodiment, wherein modules execute the concrete mode of operation in related this method
Embodiment in be described in detail, no detailed explanation will be given here.
It is understood that same or similar part can mutually refer in the various embodiments described above, in some embodiments
Unspecified content may refer to the same or similar content in other embodiments.
It should be noted that term " first ", " second " etc. are used for description purposes only in the description of the present application, without
It can be interpreted as indication or suggestion relative importance.In addition, in the description of the present application, unless otherwise indicated, the meaning of " multiple "
Refer at least two.
Any process described otherwise above or method description are construed as in flow chart or herein, and expression includes
It is one or more for realizing specific logical function or process the step of executable instruction code module, segment or portion
Point, and the range of the preferred embodiment of the application includes other realization, wherein can not press shown or discussed suitable
Sequence, including according to related function by it is basic simultaneously in the way of or in the opposite order, Lai Zhihang function, this should be by the application
Embodiment person of ordinary skill in the field understood.
It should be appreciated that each section of the application can be realized with hardware, software, firmware or their combination.Above-mentioned
In embodiment, software that multiple steps or method can be executed in memory and by suitable instruction execution system with storage
Or firmware is realized.It, and in another embodiment, can be under well known in the art for example, if realized with hardware
Any one of column technology or their combination are realized: having a logic gates for realizing logic function to data-signal
Discrete logic, with suitable combinational logic gate circuit specific integrated circuit, programmable gate array (PGA), scene
Programmable gate array (FPGA) etc..
Those skilled in the art are understood that realize all or part of step that above-described embodiment method carries
It suddenly is that relevant hardware can be instructed to complete by program, the program can store in a kind of computer-readable storage medium
In matter, which when being executed, includes the steps that one or a combination set of embodiment of the method.
It, can also be in addition, can integrate in a processing module in each functional unit in each embodiment of the application
It is that each unit physically exists alone, can also be integrated in two or more units in a module.Above-mentioned integrated mould
Block both can take the form of hardware realization, can also be realized in the form of software function module.The integrated module is such as
Fruit is realized and when sold or used as an independent product in the form of software function module, also can store in a computer
In read/write memory medium.
Storage medium mentioned above can be read-only memory, disk or CD etc..
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is contained at least one embodiment or example of the application.In the present specification, schematic expression of the above terms are not
Centainly refer to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be any
One or more embodiment or examples in can be combined in any suitable manner.
Although embodiments herein has been shown and described above, it is to be understood that above-described embodiment is example
Property, it should not be understood as the limitation to the application, those skilled in the art within the scope of application can be to above-mentioned
Embodiment is changed, modifies, replacement and variant.
Claims (10)
1. a kind of social network influence power maximization approach based on directed tree characterized by comprising
Directed tree is constructed according to the digraph of each node；
Each default initial live is calculated according to each default status information for initially enlivening each node in combination of nodes and the directed tree
Total weight of the influence that the combination of nodes that jumps generates expected final live-vertex at the end of propagating；
Select the corresponding default each node initially enlivened in combination of nodes of maximum total weight as the directed tree
Initial live-vertex.
2. the social network influence power maximization approach according to claim 1 based on directed tree, which is characterized in that described
Status information includes node type, enters side neighbours' number；The node type includes leaf node, the inside section for seed node
The internal node of point, non-seed node.
3. the social network influence power maximization approach according to claim 2 based on directed tree, which is characterized in that described
At most there are two in-degrees for internal node in directed tree.
4. the social network influence power maximization approach according to claim 3 based on directed tree, which is characterized in that described
Each default initial active section is calculated according to each default status information for initially enlivening each node in combination of nodes and the directed tree
Total weight of the influence that point combination generates expected final live-vertex at the end of propagating, comprising:
When the node v in the directed tree is leaf node and the default node initially enlivened in combination of nodes is more than or equal to 1,
Total weight is calculated according to the first formula；
First formula is
Wherein f (k, v, i) is total weight, and k is the default node initially enlivened in combination of nodes；
When the node v in the directed tree is the internal node for seed node and the default node initially enlivened in combination of nodes
Enter side neighbours u more than or equal to 1 and there are two v bands1And u2When, total weight is calculated according to the second formula；
Second formula is
When the node v in the directed tree is the internal node for seed node and the default node initially enlivened in combination of nodes
More than or equal to 1 and v has one and enters side neighbours u1When, total weight is calculated according to third formula；
The third formula is
When the node v in the directed tree is the internal node of non-seed node and the default node initially enlivened in combination of nodes
Enter side neighbours u more than or equal to 1 and there are two v bands1And u2When, total weight is calculated according to the 4th formula；
4th formula is
When the node v in the directed tree is the internal node of non-seed node and the default node initially enlivened in combination of nodes
More than or equal to 1 and v has one and enters side neighbours u1When, total weight is calculated according to the 5th formula；
5th formula is
F (k, v, i)=1+f (k, u1,i+1)。
5. the social network influence power maximization approach of directed tree according to claim 1, which is characterized in that described oriented
Tree is transformed by the digraph of each node according to binary tree property.
6. a kind of social network influence power based on directed tree maximizes device characterized by comprising
Module is constructed, for constructing directed tree according to the digraph of each node；
Computing module, for being calculated according to each default status information for initially enlivening each node in combination of nodes and the directed tree
Total weight of expected final live-vertex at the end of each default influence for initially enlivening combination of nodes generation is propagated；
Selecting module, for select the corresponding default each node initially enlivened in combination of nodes of maximum total weight as
The initial live-vertex of the directed tree.
7. the social network influence power according to claim 6 based on directed tree maximizes device, which is characterized in that described
Status information includes node type, enters side neighbours' number；The node type includes leaf node, the inside section for seed node
The internal node of point, non-seed node.
8. the social network influence power according to claim 7 based on directed tree maximizes device, which is characterized in that described
At most there are two in-degrees for internal node in directed tree.
9. the social network influence power according to claim 8 based on directed tree maximizes device, which is characterized in that described
Computing module includes:
First computing unit is leaf node for the node v in the directed tree and default initially enlivens in combination of nodes
Node be more than or equal to 1 when, total weight is calculated according to the first formula；
First formula is
Wherein f (k, v, i) is total weight, and k is the default node initially enlivened in combination of nodes；
Second computing unit, for being internal node and the default initial live for seed node as the node v in the directed tree
Node in jump combination of nodes is more than or equal to 1 and v band, and there are two enter side neighbours u1And u2When, total weight is according to the second formula
It is calculated；
Second formula is
Third computing unit, for being internal node and the default initial live for seed node as the node v in the directed tree
Node in jump combination of nodes is more than or equal to 1 and v and enters side neighbours u with one1When, total weight is carried out according to third formula
It calculates；
The third formula is
4th computing unit, for being the internal node of non-seed node as the node v in the directed tree and presetting initial live
Node in jump combination of nodes is more than or equal to 1 and v band, and there are two enter side neighbours u1And u2When, total weight is according to the 4th formula
It is calculated；
4th formula is
5th computing unit, for being the internal node of non-seed node as the node v in the directed tree and presetting initial live
Node in jump combination of nodes is more than or equal to 1 and v and enters side neighbours u with one1When, total weight is carried out according to the 5th formula
It calculates；
5th formula is
F (k, v, i)=1+f (k, u1,i+1)。
10. a kind of social network influence power based on directed tree maximizes equipment characterized by comprising
Processor, and the memory being connected with the processor；
The memory is at least used for perform claim and requires any one of 1-5 for storing computer program, the computer program
The social network influence power maximization approach based on directed tree；
The processor is for calling and executing the computer program in the memory.
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