CN106780066B - Method for evaluating influence between individuals and groups - Google Patents
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Abstract
The invention discloses a method for evaluating influence between individuals and groups, which is used for calculating a group influence algorithm by defining related concepts of influence between different users in a network. The invention has the advantages that: 1) direct influence and indirect influence are defined, interaction among nodes is fully considered, and a calculation method of the indirect influence is provided. 2) Two indexes of the influence of the individual and the group are the influence between the group and the internal individual and the influence between the group and the external individual. 3) A method for calculating the influence between a group and an internal individual is provided; 4) a method for calculating the influence between a group and an external individual is provided; 5) the method extracts and classifies the group influence into the influence between individuals and groups, is convenient for index research on the group influence, accords with social reality, and improves the accuracy of influence evaluation.
Description
Technical Field
The invention relates to a method for evaluating influence between individuals and groups, belonging to the technical field of network communication.
Background
The user group of the online social network is an informal, cross-regional and freely-developable huge social network group formed by certain social relations, social backgrounds or interests and the like. The virtual world network groups which seem to be sparsely connected have a crucial influence on public opinion guidance, social hotspot fluctuation, specific group emergence and the like. Different users in the online social network have different influences according to different factors such as individual attributes, activity degree, communication range, behavior characteristics and the like of the social users.
Although realistic interpersonal relationships can be embodied in online social networks, they are not all drivers of online social network group formation. The unique behavioral patterns of humans are rooted in the high sociality of humans, which makes humans have more complex motion patterns than randomly dispersed physical particles. It is worth emphasizing that any group or network is formed due to mutual feedback between individual relations, thereby realizing the self-adaptation of network users and the self-organization of the group. Therefore, studying group user relationships and influence of social networks is a key to deep profiling the group behavior of network users.
In recent years, researchers have conducted some research into the influence of online social networks. Early work explored and analyzed the performance of influence in social activities and relevant factors, and research sample space was less at that time, and the amount of data that can be obtained was limited. With the support of a large amount of objective data of the online social network, students begin to conduct relevant research and discussion on various problems such as the community of the users and the expressed influence, the mutual influence of the expressions in the online interaction process of the users, and the evolution of the influence along with time. User data for Twitter was analyzed by, e.g., leave e.burchard, d.fisher, and s.gilbert "The influentials: new approaches for analyzing influence on Twitter" (Web Ecology Project,2009,4(2):1-18.), etc., and The influence of The user was classified into two types: conversation-based influence (conversation-based) and content-based influence (content-based). According to The propagation characteristics of The interactive information on The social network, Page L, Brin S, Motwani R, The Page rank circulation and The Bringing order to The web, The influence of The user is scored by using The forwarding sequence of The information. Kwak H, Lee C, Park H "when is Twitter, a social network or a news media" (Proceedings of the 19th international conference on World with web. ACM,2010: 591-. Yang and Leskovec, Modeling information distribution in information networks (Proceedings of 2010 IEEE International Conference on Data mining. Sydney, Ausrali, 2010: 599-grade 608) consider that the information propagation process is controlled by the influence of the user, and have no necessary connection with the explicit network topology and the interconnection among users, and establish a linear influence model LIM for representing the relation between the influence of the user and other users which have been influenced in the past. In the research on influence strength among users in an online social network (cloud crystal, Shanghai university of transportation, 2013), the existing model is modified by introducing the Bayesian probability theory and the virtual forwarding concept, and a Bayesian influence strength model is provided. The research on the influence achieves certain results, but relatively few group influence measures and indexes are described, and no relative systematic method is used for describing and calculating the group influence. The present invention can solve the above problems well.
Disclosure of Invention
The invention aims to provide an influence evaluation method between individuals and a group aiming at the defects of the prior art, which fully considers the relation between user influences and provides influence indexes of nodes inside and outside the group on the group so as to comprehensively evaluate the influence of the group.
The technical scheme adopted by the invention for solving the technical problems is as follows: the method is applied to the network of the user interaction relationship of the online social network, utilizes the weighting network to simulate the group with known direct influence, fully considers the mechanism and the way of the action of the influence force, and provides the influence force relationship and the calculation method of the indirect influence force, the group interior and the group exterior node.
The method comprises the following steps:
step 1: calculating direct influence and indirect influence among different users by adopting the defined influence among the users;
step 2: calculating the influence of the nodes in the group on the group influence by adopting the defined nodes and the group influence;
and step 3: and calculating the influence of the external individual on the group by adopting the defined node and the influence of the nodes outside the group.
And 4, step 4: and calculating the influence of the group on the external individual by adopting the defined node and the influence of the nodes outside the group.
Further, in step 1 of the present invention, direct influence and indirect influence are defined: the direct influence is the direct influence between all pairs of nodes with direct interaction history, and the indirect influence is the resultant influence between all pairs of nodes without direct interaction history. The formula for the calculation of the influence is given:
calculating the influence of the node i on the node j can be expressed as:
f (i, j) represents the indirect influence degree of the node i to the node j; pijRepresenting nodes i to jAll paths, k, v, denote PijA node in the path; alpha is a synthesis factor (which can be considered as a weighted average of the direct influence between all nodes). If node i has direct interaction with node j, then FijIs the direct influence of node i on node j; if node i and node j do not have direct interaction, FijThe degree of indirect influence of node i on node j.
Further, in step 2 of the present invention, the influence between the internal individual and the group is defined, and the calculation of the influence between the internal individual and the group is given. If the influence F (i, R) of i in the population R is calculated, the direct influence degree and the indirect influence degree of the node on the internal nodes of the population are integrated, and the method can be expressed as follows:
F(i，R)＝β∑j∈R，i≠jFij (2)
f (i, R) represents the influence of the node i in the population R; fijRepresenting the influence degree of the node i on the node j; beta is a synthesis factor
(it can be considered to weight average the influence of i on all nodes).
Furthermore, in step 3 of the present invention, the influence of the external individual on the population is calculated by using the defined node and the influence of the nodes outside the population. The influence Y (i, R) of the external individual i on the population R can be expressed as:
Y(i，R)＝γ(α∑j∈R，j∈V(i)Fij+β∑j∈R，j∈V(i)F(j，R)) (3)
v (i) represents a set of nodes that have interacted directly with i. FijRepresenting the influence of the node i on the node j; f (j, R) is the influence of the node j in the population R; gamma is a comprehensive factor.
Furthermore, in step 3 of the present invention, the influence of the defined nodes and nodes outside the group is calculated to calculate the influence of the group on the external individual. The influence Y (R, i) of the population R on the node i can be expressed as:
Y(R，i)＝δ∑j∈R，j∈V(i)Fji (4)
v (i) represents a set of nodes that have interacted directly with i. FjiRepresenting the influence of the node j on the node i; delta is a synthesisA factor.
Has the advantages that:
1. the invention adopts the theory of complex network, and expresses the direct influence network among the nodes by the weighted directed network of the complex network, thereby fully expressing the relation among the influence of the nodes.
2. The invention abstracts the influence among users into direct influence and indirect influence, and is closer to the social reality of social networks.
3. The invention extracts two indexes of group influence: the internal node and group influence index and the external node and group influence index are beneficial to measuring the internal constraint and influence of the group and the capacity of the group for external expansion and enlargement, and provide quantifiable indexes for further studying group influence and group behaviors.
4. The method for evaluating the group influence provided by the invention evaluates the group influence from multiple aspects and accords with social reality.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of an influence model of individuals within a population.
FIG. 3 is a schematic diagram of a model of the influence of an individual on a population.
FIG. 4 is a diagram of an example of node a versus other nodes in the present invention.
FIG. 5 is a graph of an example of the effect of an external individual on a population.
FIG. 6 is a graph of an example of the effect of a population on an external individual.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings.
The invention provides an influence evaluation method between individuals and groups, which fully considers the relationship between user influences and provides influence indexes of nodes inside and outside the groups on the groups so as to comprehensively evaluate the influence of the groups.
The method is applied to the network of the user interaction relationship of the online social network, utilizes the weighting network to simulate the group with known direct influence, fully considers the mechanism and the way of the action of the influence force, and provides the influence force relationship and the calculation method of the indirect influence force, the group interior and the group exterior node. The method specifically comprises the following steps:
step 1: calculating direct influence and indirect influence among different users by adopting the defined influence among the users, wherein the calculation of the influence presents asymmetry;
step 2: calculating the influence of the nodes in the group on the group influence by adopting the defined nodes and the group influence;
and step 3: and calculating the influence of the external individual on the group by adopting the defined node and the influence of the nodes outside the group.
And 4, step 4: and calculating the influence of the group on the external individual by adopting the defined node and the influence of the nodes outside the group.
In step 1 of the present invention, direct influence and indirect influence are defined: the direct influence is the direct influence between all pairs of nodes with direct interaction history, and the indirect influence is the resultant influence between all pairs of nodes without direct interaction history. The formula for the calculation of the influence is given:
calculating the influence of the node i on the node j can be expressed as:
Fijrepresenting the indirect influence degree of the node i to the node j, and F (i, j) representing the indirect influence degree of the node i to the node j; pijRepresenting all paths from node i to node j, k, v representing PijA node in the path; alpha is a synthesis factor (which can be considered as a weighted average of the direct influence between all nodes). If node i has direct interaction with node j, then FijIs the direct influence of node i on node j; if node i and node j do not have direct interaction, FijThe degree of indirect influence of node i on node j.
Further, in step 2 of the present invention, the influence between the internal individual and the group is defined, and the calculation of the influence between the internal individual and the group is given. If the influence F (i, R) of i in the population R is calculated, the direct influence degree and the indirect influence degree of the node on the internal nodes of the population are integrated, and the method can be expressed as follows:
F(i，R)＝β∑j∈R，i≠jFij (2)
f (i, R) represents the influence of the node i in the population R; fijRepresenting the influence degree of the node i on the node j; beta is a synthesis factor
(it can be considered to weight average the influence of i on all nodes).
In step 3, the influence of the external individual on the group is calculated by adopting the defined nodes and the influence of the nodes outside the group. The influence Y (i, R) of the external individual i on the population R can be expressed as:
Y(i，R)＝γ(α∑j∈R，j∈V(i)Fij+β∑j∈R，j∈V(i)F(j，R)) (3)
v (i) represents a set of nodes that have interacted directly with i. FijRepresenting the influence of the node i on the node j; f (j, R) is the influence of the node j in the population R; gamma is a comprehensive factor.
Furthermore, in step 3 of the present invention, the influence of the defined nodes and nodes outside the group is calculated to calculate the influence of the group on the external individual. The influence Y (R, i) of the population R on the node i can be expressed as:
Y(R，i)＝δ∑j∈R，j∈V(i)Fji (4)
v (i) represents a set of nodes that have interacted directly with i. FjiRepresenting the influence of the node j on the node i; delta is a synthesis factor.
As shown in FIG. 4, the direct influence of node a on node b is FabThe direct influence of node b on node d is FbdTwo influencing paths respectively P exist between the node a and the node dabdAnd PacdThe indirect influence degree of the node a to the node d obtained by the comprehensive calculation of the two influence paths is as follows:
Fad＝α1Fab·Fbd+α2Fac·Fcd
α1，α2is a composite weight factor (which may be considered as a weighted average of the direct influence between all nodes).
Different nodes have different influences on the population, some nodes may have influences on most nodes in the population, and some nodes may have little influence on the population. The larger the influence of a node on other nodes in the group is, and the larger the influence of the node on the node is, the larger the influence in the group of nodes is. As shown in fig. 4, the influence F (R, a) of the group R on the node a is the influence of each node in the group on the node a:
F(a,R)＝β1Fab+β2Fad+β3Fac
β1，β2，β3is the weight of each influence, and is the weight of each influence.
Due to the asymmetry of the influence, there is a difference in the influence of the population on the external individuals, as shown in fig. 5. If the node d is outside the group R, the node d directly interacts with the nodes c and e to influence the group R, so that the influence of the node d on the group R plays a key role in the points c and e. The influence of the external node d on the population R comprises two important factors, namely the influence F of the external node d on the nodes c and e directly interacting in the population Rdc，FdeAnd c, e influence in population R F (c, R) and F (e, R).
The influence Y (d, R) of the external individual d on the population R can be expressed as:
Y(d,R)＝ε1Fdc·F(c,R)+ε2Fde·F(e,R)
ε1,ε2is the weight of each influence.
The influence of the population on the external individual depends mainly on the influence of the nodes within the population that interact directly with the external individual. As shown in fig. 6, even if c and e have a high influence within the population or the influence of other nodes in the population on the node c and e is high, the influence of c and e on the node d is not high, and the influence of the population R on the node d is still not high. Therefore, the influence of the population R on the node d depends on the direct influence of the nodes c and e on the node d. The influence Y (R, d) of the population R on the node d can be expressed as:
Y(R,d)＝δ1Fcd+δ2Fed
δ1，δ2is the weight of each influence.
Claims (1)
1. A method for evaluating influence between individuals and groups is applied to a network of user interaction relations of an online social network, a group with known direct influence is simulated by using a weighting network, the mechanism and the path of action of influence are fully considered, the magnitude of indirect influence is given, and the influence relation and the calculation method between the inside of the group and nodes outside the group comprise the following steps:
step 1: the method comprises the following steps of calculating direct influence and indirect influence among different users by adopting defined influence among the users, wherein the calculation of the influence presents asymmetry, the indirect influence of a calculation node fully considers the mutual influence of the nodes, and the calculation formula is as follows:
calculating the influence of the node i on the node j can be expressed as:
Fijrepresents the influence of node i on node j, PijRepresenting all paths from node i to node j, k, v representing PijA node in the path; alpha is a comprehensive factor; if node i has direct interaction with node j, then FijIs the direct influence of the node i on the node j, if the node i and the node j have no direct interaction, then FijIs the indirect influence of node i on node j;
step 2: and calculating the influence of the nodes in the group on the group by adopting the defined nodes and group influence, wherein the influence F (i, R) of i in the group R is the synthesis of the direct influence and the indirect influence of the nodes on the nodes in the group, and can be expressed as follows:
F(i，R)＝β∑j∈R，i≠jFij (2)
f (i, R) represents the influence of the node i in the population R; fijRepresenting the influence of the node i on the node j; beta is a comprehensive factor;
and step 3: calculating the influence of the external individual on the population by adopting the defined influence of the nodes and the nodes outside the population, wherein the influence of the external individual on the population R comprises two important parts, namely the influence of the external individual on the directly interactive individual in the population R and the influence of the individual in the population R;
thus, the influence Y (i, R) of the external individual i on the population R is expressed as:
Y(i，R)＝γ(α∑j∈R，j∈V(i)Fij+β∑j∈R，j∈V(i)F(j，R)) (3)
v (i) represents a set of nodes that have interacted directly with i, FijRepresenting the influence of the node i on the node j, wherein F (j, R) is the influence of the node j in the group R; gamma is a comprehensive factor;
and 4, step 4: and calculating the influence of the group on the external individual by adopting the defined node and the influence of the nodes outside the group, wherein the influence Y (R, i) of the group R on the node i is expressed as follows:
Y(R，i)＝δ∑j∈R，j∈V(i)Fji (4)
v (i) represents a set of nodes that have interacted directly with i, FjiRepresenting the influence of the node j on the node i; delta is a synthesis factor.
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