CN103678669A - Evaluating system and method for community influence in social network - Google Patents
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Abstract
The invention relates to an evaluating system and method for community influence in a social network. The method comprises the steps that a social network chart with social network users as nodes and user relationships as sides is built; according to the social network chart, the community structure of the social network is obtained by carrying out community division through the label propagation algorithm; according to the social network chart and matrixes which communities belong to, the parameter of the community influence is calculated, and the initial influence of each community is generated; according to the transmission probability model of the influence, an influence transmission probability matrix is generated; according to the influence transmission probability matrix and the community influence iterative computation model, the community influence is iterated and upgraded until the iteration end condition is met, the influence value of each community is obtained, and the sequence of the community influence, namely, the influence estimation result of each community in the social network is obtained after normalization. The system and method can effectively analyze the distribution of the community influence in the social network and can be used for high-influence community mining, thereby being capable of being applied to the fields of network marketing and the like.
Description
Technical field
The present invention relates to social networks technical field, particularly a kind of community influence evaluating system and the method in social networks.
Background technology
Social influence power refers to because user, tissue or community and other users, tissue or community etc. have social networks, a kind of phenomenon that causes self behavior to change with the variation of other users, tissue or community.Social influence power is a kind of phenomenon common in social networks.In community network, various factors all may exert an influence to influence power.By the influence power of the individuality such as node, community in social networks is analyzed, the core node with material impact power and core community in social networks be can find, enterprise's trade marketing, advertisement fixing can be used for to numerous areas such as input, the recommendation of speech channel, public sentiment monitoring.
At present the research of social influence power is mainly concentrated on the influence power analysis of individual nodes, for different structure or dissimilar community network, by in conjunction with factors such as multiple network architectural feature, nodal community or behavior relations, construct corresponding model and algorithm the social influence power of individual nodes is assessed.The model adopting mainly comprises the assessment models based on structure attributes such as degree centrality, distance center, betweenness centrality.As the influence power of user in the employing users' such as Meeyoung Cha in-degree, forwarding number and three parameter evaluation Twitter networks of number of references; Xiong Z etc. has designed UCI (User Community Influence) model for assessment of the influence power between user; Liu Jianguo etc. are based on k-shell decomposition method assessment node influence power.
Occurred in recent years some appraisal procedures for community influence, BELAK V etc. calculate the cross influence power between community based on the degree of membership between the interactivity between member and member and community in social networks; The thought based on Information Communication such as Eftekhar M is found the stronger community of transmission capacity.
To sum up, there is more perfect technology and method in the influence power analysis for user's individuality in social networks, but the method for analyzing for other influence power of community-level in social networks is also relatively less, and lack the multianalysis assessment to the influence power of social networks Zhong Ge community, in the face of the scene of extensive social networks, existing method is in analytical effect and efficiency, to be all difficult to meet the demands.
Summary of the invention
The object of the invention is to overcome the deficiencies in the prior art, community influence evaluating system and method in a kind of social networks are provided, this system and method is conducive to improve effect and the efficiency of community influence assessment.
For achieving the above object, technical scheme of the present invention is: the community influence evaluating system in a kind of social networks, and described system comprises that social network diagram constructing module, community divide module, community network constructing module, the initial influence power generation module in community, community influence probability of spreading generation module and community influence estimation module;
Social network diagram constructing module, take for constructing the social network diagram that social networks user is limit as node, customer relationship;
Community divides module, for according to social network diagram, adopts label propagation algorithm to carry out community's division, obtains the community structure of social networks;
Community network constructing module, for according to the community structure of social networks, generates community network figure, and structure represents that the community of node and community's membership is subordinate to matrix;
The initial influence power generation module in community, for being subordinate to matrix according to community network Tu Ji community, calculates community influence parameter, generates the initial influence power in community of each community;
Community influence probability of spreading generation module, for according to adopted influence power probability of spreading model, generates influence power probability of spreading matrix;
Community influence estimation module, influence power probability of spreading matrix, the initial influence power in community obtaining for basis and the community influence iterative computation model adopting, iteration is upgraded community influence, until meet stopping criterion for iteration, after normalization, obtain community influence sequence, i.e. the community influence assessment result of social networks Zhong Ge community.
The present invention also provides the appraisal procedure of the community influence in a kind of social networks, and described method comprises the steps:
Steps A: read social network data, structure be take social networks user as node, the social network diagram that customer relationship is limit;
Step B: according to social network diagram, adopt label propagation algorithm to carry out community's division, obtain the community structure of social networks;
Step C: according to the community structure of social networks, generate community network figure, calculate and represent that the community of node and community's membership is subordinate to matrix;
Step D: be subordinate to matrix according to community network Tu Ji community, calculate community influence parameter, generate the initial influence power in community of each community;
Step e: according to adopted influence power probability of spreading model, calculate influence power probability of spreading matrix, obtain the influence power probability of spreading between any Liang Ge community;
Step F: according to influence power probability of spreading matrix, the initial influence power in community and the community influence iterative computation model that adopts, iteration is upgraded community influence, until meet stopping criterion for iteration, obtains the influence power value of each community;
Step G: the influence power value to each community obtaining is normalized, and obtains community influence sequence, i.e. the community influence assessment result of social networks Zhong Ge community.
Further, when step B is by social networks
g=(
v,
e) be divided into
kindividual community, obtains after the community structure of social networks, in step C, uses community network figure
cG=(
c,
cE) represent the community structure of social networks, wherein
c=
c 1,
c 2...,
c k represent that the community that division obtains gathers,
for community network figure
cGlimit collection, by
ethe limit subset of the different communities of middle connection forms, and community is subordinate to matrix
mfor
matrix, represents node and intercommunal membership, and matrix element is defined as:
If i.e. node
ibe under the jurisdiction of community
q,
m i,
q =1, otherwise
m i,
q =0.
Further, described step D specifically comprises the following steps:
Step D1: social network diagram is provided
g=(
v,
e), community network figure
cG=(
c,
cE), community is subordinate to matrix
m;
Step D2: the community's incidence matrix that calculates degree in close relations between reflection community
r;
Step D3: calculate intercommunal influence degree matrix
iA;
Step D4: according to the influence degree matrix obtaining, calculate the initial influence power in community of each community
.
Further, in described step D2, community's incidence matrix
rfor
matrix, the computing formula of matrix element is as follows:
Wherein,
a i,
j for social network diagram
gadjacency matrix element,
r i,
j for connecting community
pand community
qlimit collection weight and, i.e. community
pand community
qbetween incidence relation number, the associated level of intimate of reflection between community.
Further, in described step D3, influence degree matrix
iArepresent community's influence degree each other, be defined as community's incidence matrix
rand acting matrix
hadamard long-pending, computing formula is:
Acting matrix wherein
element definition be:
Wherein,
n i,
j represent community
iand community
jthere is associated nodes, |
v j | be community
jnodes.
Further, in described step D4, the initial influence power in community is defined as the influence degree summation of this community to its all neighbours community, and the initial influence power computing formula in community is:
Wherein,
nE(
p) expression community
pneighbours community set.
Further, in described step e, community influence probability of spreading matrix
tthe computing formula of matrix element as follows:
Wherein,
for influence power contribution rate, represent community
pinfluence power be diffused into community
qprobability,
definition only consider two intercommunal influence power transition probabilities, embodiment be the local influence of community, be defined as follows:
This character has guaranteed the consistance of each community influence spreading probability;
s p,
q represent community
qneighbours community
pdui Chu community
qneighbours community outside the influence power probability of spreading of community, considered that community influence is diffused into the indirect influence to other communities behind neighbours community, i.e. the global impact ability of community, is defined as follows:
Wherein |
nE(
p) | represent community
pneighbours community number,
represent community
pand community
qcommon neighbours' number.
Further, in described step F, community influence iterative computation model definition is as follows:
Wherein,
tfor community influence probability of spreading matrix,
kfor community's number,
for damping factor, for the calculating to community influence, revise,
trepresent iterations,
nfor maximum iteration time;
Stopping criterion for iteration is defined as the influence power phase difference of twice iteration in algorithm front and back
be less than threshold value
or iterations surpasses maximum iteration time
n;
the maximal value that refers to all communities twice iteration influence power difference in front and back, is defined as:
Further, in described step G, normalization is that each community influence value is mapped to [0,1] interval, is convenient to the quantitative and qualitative of individual community influence to estimate, the linear normalization function definition of employing is:
Wherein,
infl i be
ithe influence power value of individual community,
minInflfor community influence minimum value,
maxInflfor community influence maximal value.
Compared to prior art, the invention has the beneficial effects as follows: based on community influence factors such as neighbours' quality, community member's number, the neighbours of community numbers, adopt random walk model, constructed community influence probability of spreading metastasis model and influence power assessment alternative manner.To sum up, system and method for the present invention can be assessed the influence power of community in social networks rationally and effectively.
Accompanying drawing explanation
Fig. 1 is the modular structure schematic diagram of system of the present invention.
Fig. 2 is the realization flow figure of the inventive method.
Embodiment
Below in conjunction with drawings and the specific embodiments, the present invention is described in further detail.
Fig. 1 is the modular structure schematic diagram of the community influence evaluating system in social networks of the present invention.As shown in Figure 1, described system comprises: social network diagram constructing module 100, community divide module 200, community network constructing module 300, the initial influence power generation module 400 in community, community influence probability of spreading generation module 500, community influence estimation module 600.
Social network diagram constructing module 100, take for constructing the social network diagram that social networks user is limit as node, customer relationship; Community divides module 200, for according to social network diagram, adopts label propagation algorithm to carry out community's division, obtains the community structure of social networks; Community network constructing module 300, for according to the community structure of social networks, generates community network figure, and structure represents that the community of node and community's membership is subordinate to matrix; The initial influence power generation module 400 in community, for being subordinate to matrix according to community network Tu Ji community, calculates community influence parameter, generates the initial influence power in community of each community; Community influence probability of spreading generation module 500, for according to adopted influence power probability of spreading model, generates influence power probability of spreading matrix; Community influence estimation module 600, influence power probability of spreading matrix, the initial influence power in community obtaining for basis and the community influence iterative computation model adopting, iteration is upgraded community influence, until meet stopping criterion for iteration, after normalization, obtain community influence sequence, i.e. the community influence assessment result of social networks Zhong Ge community.
Fig. 2 is the realization flow schematic diagram of the community influence appraisal procedure in social networks of the present invention.As shown in Figure 2, described method comprises the steps:
Steps A: read social network data, structure be take social networks user as node, the social network diagram that customer relationship is limit.
As for microblogging network, a node using each microblogging registered user in social networks, usings mutual concern between user, comment relation as a limit in social networks; As for collaborative network, a node using each author in network, usings two authors cooperation relation that at least co-present is crossed one piece of article as a limit in social networks.Adopt the adjacency matrix A of the data structure storage social network diagram of sparse matrix.
Concrete, the element definition in adjacency matrix A is:
Step B: according to social network diagram, adopt label propagation algorithm to carry out community's division, obtain the community structure of social networks.
Concrete, can adopt label propagation algorithm (Label Propagation Algorithm) to carry out community's division to social networks, label propagation algorithm is a kind ofly first each node to be carried out to label initialization, for each node, give a unique label, with certain update rule, upgrade successively afterwards the label of each node; Constantly repeat this step, until there is no node generation tag update in inferior iteration is upgraded, algorithm finishes, and the node with same label belongs to same community, obtains community's division result of social networks.The update rule of label is as follows:
Wherein,
it is node
ucurrent tag number,
be
vtag number after renewal, if a plurality of label satisfies condition, therefrom selects a label to give node at random
v.
Step C: according to the community structure of social networks, generate community network figure, calculate and represent that the community of node and community's membership is subordinate to matrix.
Concrete, when step B is by social networks
g=(
v,
e) be divided into
kindividual community, obtains after the community structure of social networks, in step C, uses community network figure
cG=(
c,
cE) represent the community structure of social networks, wherein
c=
c 1,
c 2...,
c k represent that the community that division obtains gathers,
for community network figure
cGlimit collection, by
ethe limit subset of the different communities of middle connection forms, and community is subordinate to matrix
mfor
matrix, represents node and intercommunal membership, and matrix element is defined as:
If i.e. node
ibe under the jurisdiction of community
q,
m i,
q =1, otherwise
m i,
q =0.
Step D: be subordinate to matrix according to community network Tu Ji community, calculate community influence parameter, generate the initial influence power in community of each community.
Described step D specifically comprises the following steps:
Step D1: social network diagram is provided
g=(
v,
e), community network figure
cG=(
c,
cE), community is subordinate to matrix
m.
Step D2: the community's incidence matrix that calculates degree in close relations between reflection community
r.Community's incidence matrix has reflected intercommunal degree in close relations, in real community network, if the internal node of Liang Ge community is more across community's incidence relation number, represents that the relation of Zhe Liangge community is just closer.
Concrete, in described step D2, community's incidence matrix
rfor
matrix, the computing formula of matrix element is as follows:
Wherein,
a i,
j for social network diagram
gadjacency matrix element,
r i,
j for connecting community
pand community
qlimit collection weight and, i.e. community
pand community
qbetween incidence relation number, the associated level of intimate of reflection between community.
Step D3: calculate intercommunal influence degree matrix
iA.
Concrete, in described step D3, influence degree matrix
iArepresent community's influence degree each other, be defined as community's incidence matrix
rand acting matrix
hadamard long-pending, computing formula is:
Acting matrix wherein
element definition be:
Wherein,
n i,
j represent community
iand community
jthere is associated nodes, |
v j | be community
jnodes.
If the relation of Liang Ge community is closer, influencing each other between them is just larger.But because the scale of Liang Ge community may be not identical, the degree that influences each other between them should be also not identical, considers following scene, community
c 1zhong Huibei community
c 2the member who has influence on accounts for that they are whole 1/3, and community
c 2zhong Bei community
c 1the member who has influence on accounts for that they are whole 3/5, even if community
c 1with
c 2between weight equate, but community
c 1to community
c 2influence degree compare community
c 2to community
c 1influence degree large.Acting matrix element has embodied the ratio that has associated nodes Shuo Zhan community node sum between community, so the influence degree defined matrix between community is community's incidence matrix
and acting matrix
hadamard long-pending.
Step D4: according to the influence degree matrix obtaining, calculate the initial influence power in community of each community
.
Concrete, in described step D4, the initial influence power in community is defined as the influence degree summation of this community to its all neighbours community, and the initial influence power computing formula in community is:
Wherein,
nE(
p) expression community
pneighbours community set.
Step e: according to adopted influence power probability of spreading model, calculate influence power probability of spreading matrix, obtain the influence power probability of spreading between any Liang Ge community.
Concrete, in described step e, community influence probability of spreading matrix
tthe computing formula of matrix element as follows:
Wherein,
for influence power contribution rate, represent community
pinfluence power be diffused into community
qprobability.
Influence power contribution rate is based on following thought: if neighbours' influence power of Yi Ge community is more and more stronger, the influence power of this community itself also can promote thereupon so., each node in figure represents Yi Ge community, supposes that the influence power of the E of community becomes large, in the situation that the E of community is constant to the influence degree of the A of community, the influence power that is diffused into the A of community from the E of community becomes large, and the influence power of the A of community is got a promotion.Wherein, the ratio that the influence power that the E of community is diffused into the A of community accounts for the whole influence powers of the E of community is equivalent to the influence power contribution rate of the E of community to the A of community.
computing formula as follows:
This character has guaranteed the consistance of each community influence spreading probability.
Wherein |
nE(
p) | represent community
pneighbours community number,
represent community
pand community
qcommon neighbours' number.
Step F: according to influence power probability of spreading matrix, the initial influence power in community and the community influence iterative computation model that adopts, iteration is upgraded community influence, until meet stopping criterion for iteration, obtains the influence power value of each community.
Concrete, in described step F, community influence iterative computation model definition is as follows:
Wherein,
tfor community influence probability of spreading matrix,
kfor community's number,
for damping factor, for the calculating to community influence, revise,
trepresent iterations,
nfor maximum iteration time;
Stopping criterion for iteration is defined as the influence power phase difference of twice iteration in algorithm front and back
be less than threshold value
or iterations surpasses maximum iteration time
n;
the maximal value that refers to all communities twice iteration influence power difference in front and back, is defined as:
Step G: the influence power value to each community obtaining is normalized, and obtains community influence sequence, i.e. the community influence assessment result of social networks Zhong Ge community.
Concrete, in described step G, normalization is that each community influence value is mapped to [0,1] interval, is convenient to the quantitative and qualitative of individual community influence to estimate, the linear normalization function definition of employing is:
Wherein,
infl i be
ithe influence power value of individual community,
minInflfor community influence minimum value,
maxInflfor community influence maximal value.
Community influence evaluating system and method in social networks of the present invention, based on community influence factors such as neighbours' quality, community member's number, the neighbours of community books, adopt random walk model, constructed community influence probability of spreading metastasis model and influence power assessment alternative manner.To sum up, system and method for the present invention can be assessed the influence power of community in social networks rationally and effectively.
Be more than preferred embodiment of the present invention, all changes of doing according to technical solution of the present invention, when the function producing does not exceed the scope of technical solution of the present invention, all belong to protection scope of the present invention.
Claims (10)
1. the community influence evaluating system in social networks, is characterized in that, described system comprises:
Social network diagram constructing module, take for constructing the social network diagram that social networks user is limit as node, customer relationship;
Community divides module, for according to social network diagram, adopts label propagation algorithm to carry out community's division, obtains the community structure of social networks;
Community network constructing module, for according to the community structure of social networks, generates community network figure, and structure represents that the community of node and community's membership is subordinate to matrix;
The initial influence power generation module in community, for being subordinate to matrix according to community network Tu Ji community, calculates community influence parameter, generates the initial influence power in community of each community;
Community influence probability of spreading generation module, for according to adopted influence power probability of spreading model, generates influence power probability of spreading matrix;
Community influence estimation module, influence power probability of spreading matrix, the initial influence power in community obtaining for basis and the community influence iterative computation model adopting, iteration is upgraded community influence, until meet stopping criterion for iteration, after normalization, obtain community influence sequence, i.e. the community influence assessment result of social networks Zhong Ge community.
2. the community influence appraisal procedure in social networks, is characterized in that, described method comprises the steps:
Steps A: read social network data, structure be take social networks user as node, the social network diagram that customer relationship is limit;
Step B: according to social network diagram, adopt label propagation algorithm to carry out community's division, obtain the community structure of social networks;
Step C: according to the community structure of social networks, generate community network figure, calculate and represent that the community of node and community's membership is subordinate to matrix;
Step D: be subordinate to matrix according to community network Tu Ji community, calculate community influence parameter, generate the initial influence power in community of each community;
Step e: according to adopted influence power probability of spreading model, calculate influence power probability of spreading matrix, obtain the influence power probability of spreading between any Liang Ge community;
Step F: according to influence power probability of spreading matrix, the initial influence power in community and the community influence iterative computation model that adopts, iteration is upgraded community influence, until meet stopping criterion for iteration, obtains the influence power value of each community;
Step G: the influence power value to each community obtaining is normalized, and obtains community influence sequence, i.e. the community influence assessment result of social networks Zhong Ge community.
3. the community influence appraisal procedure in a kind of social networks according to claim 2, is characterized in that, when step B is by social networks
g=(
v,
e) be divided into
kindividual community, obtains after the community structure of social networks, in step C, uses community network figure
cG=(
c,
cE) represent the community structure of social networks, wherein
c=
c 1,
c 2...,
c k represent that the community that division obtains gathers,
for community network figure
cGlimit collection, by
ethe limit subset of the different communities of middle connection forms, and community is subordinate to matrix
mfor
matrix, represents node and intercommunal membership, and matrix element is defined as:
If i.e. node
ibe under the jurisdiction of community
q,
m i,
q =1, otherwise
m i,
q =0.
4. the community influence appraisal procedure in a kind of social networks according to claim 2, is characterized in that, described step D specifically comprises the following steps:
Step D1: social network diagram is provided
g=(
v,
e), community network figure
cG=(
c,
cE), community is subordinate to matrix
m;
Step D2: the community's incidence matrix that calculates degree in close relations between reflection community
r;
Step D3: calculate intercommunal influence degree matrix
iA;
Step D4: according to the influence degree matrix obtaining, calculate the initial influence power in community of each community
.
5. the community influence appraisal procedure in a kind of social networks according to claim 4, is characterized in that, in described step D2, and community's incidence matrix
rfor
matrix, the computing formula of matrix element is as follows:
Wherein,
a i,
j for social network diagram
gadjacency matrix element,
r p,
q for connecting community
pand community
qlimit collection weight and, i.e. community
pand community
qbetween incidence relation number, the associated level of intimate of reflection between community.
6. the community influence appraisal procedure in a kind of social networks according to claim 4, is characterized in that, in described step D3, and influence degree matrix
iArepresent community's influence degree each other, be defined as community's incidence matrix
rand acting matrix
hadamard long-pending, computing formula is:
Acting matrix wherein
element definition be:
Wherein,
n i,
j represent community
iand community
jthere is associated nodes, |
v j | be community
jnodes.
7. the community influence appraisal procedure in a kind of social networks according to claim 4, it is characterized in that, in described step D4, the initial influence power in community is defined as the influence degree summation of this community to its all neighbours community, and the initial influence power computing formula in community is:
Wherein,
nE(
p) expression community
pneighbours community set.
8. the community influence appraisal procedure in a kind of social networks according to claim 2, is characterized in that, in described step e, and community influence probability of spreading matrix
tthe computing formula of matrix element as follows:
Wherein,
for influence power contribution rate, represent community
pinfluence power be diffused into community
qprobability,
definition only consider two intercommunal influence power transition probabilities, embodiment be the local influence of community, be defined as follows:
there is following character:
This character has guaranteed the consistance of each community influence spreading probability;
s p,
q represent community
qneighbours community
pdui Chu community
qneighbours community outside the influence power probability of spreading of community, considered that community influence is diffused into the indirect influence to other communities behind neighbours community, i.e. the global impact ability of community, is defined as follows:
Wherein |
nE(
p) | represent community
pneighbours community number,
represent community
pand community
qcommon neighbours' number.
9. the community influence appraisal procedure in a kind of social networks according to claim 2, is characterized in that, in described step F, community influence iterative computation model definition is as follows:
Wherein,
tfor community influence probability of spreading matrix,
kfor community's number,
for damping factor, for the calculating to community influence, revise,
trepresent iterations,
nfor maximum iteration time;
Stopping criterion for iteration is defined as the influence power phase difference of twice iteration in algorithm front and back
be less than threshold value
or iterations surpasses maximum iteration time
n;
the maximal value that refers to all communities twice iteration influence power difference in front and back, is defined as:
10. the community influence appraisal procedure in a kind of social networks according to claim 2, it is characterized in that, in described step G, normalization is that each community influence value is mapped to [0,1] interval, be convenient to the quantitative and qualitative of individual community influence to estimate, the linear normalization function definition of employing is:
Wherein,
infl i be
ithe influence power value of individual community,
minInflfor community influence minimum value,
maxInflfor community influence maximal value.
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|---|---|---|---|
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|---|---|---|---|
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