CN112966191A - Method for acquiring new media platform network information propagation weak connection node - Google Patents
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Abstract
The invention discloses a method for acquiring a new media platform network information transmission weak connection node, which comprises the steps of firstly constructing a transmission network graph structure according to a new media platform network; constructing an adjacency matrix of the graph according to the graph structure of the propagation network; defining an objective function of the weak connection node according to the characteristics of the weak connection structure; constructing a degree matrix of the graph according to the graph structure of the propagation network, generating a corresponding Laplacian matrix of the graph according to the adjacency matrix of the graph and the degree matrix of the graph, and then calculating a Fielder eigenvector of the Laplacian matrix of the graph; and analyzing the Fielder eigenvector, positioning the weak connection node in the middle part of the Fielder eigenvector of the Laplace matrix of the graph, and obtaining the weak connection node of the propagation network by using a heuristic algorithm. The method has a certain application prospect for finding the weak connection node in the new media platform propagation network, no matter the rumor is managed and controlled or the product is marketed.
Description
Technical Field
The invention relates to the technical field of internet, in particular to a method for acquiring a new media platform network information transmission weak connection node.
Background
The new media platforms such as microblogs, WeChat, tremble, fast hands and the like have increased explosively in recent years, and the way for people to acquire information is changed. In the social network structure formed by the new media platforms, a class of nodes called weak connection plays an important role in information dissemination.
The theory of weak links (weak ties) was proposed by Mark grandvetter in The earlier published "The strength of weak ties". He believes that weak links are able to cross different social groups more than strong links and therefore reach more people, crossing a greater social distance, in which Granovetter proposes a "Bridge" concept. A bridge refers to a unique path between two individuals. Thus, a strong connection between two individuals must not be a bridge. All bridges must be weakly connected. The significance of a bridge is that it can join two different social groups. Thus, by acting as a weak link to the bridge, the individual can reach more people and traverse greater social distances. The information dissemination of the weak link nodes in the new media platform plays an important role, because the weak link nodes occupy important positions in the network structure and play a key role in the information dissemination.
The node d is a node in a weak connection relationship, as shown in fig. 1, which is a node in a weak connection relationship, or a weak connection node for short, that is, a node with a small degree but an important position. The weak connection node plays a great role in the social network information propagation process. In fig. 2, the degree of the node d is small and not important, but the position occupied by the node is important when the node d is placed in the whole network structure, and the node d plays a role of connecting the left part (nodes a, b and c) and the right part (nodes e, f, g, h, i and j), and the behavior of the node d can directly influence the propagation range of information no matter the information is propagated on any side.
Although node e is also critical to the propagation effect, the role of node e is obvious (it is a high number of degrees relative to other nodes), but it is obvious that d also plays a very large role in information propagation.
The concept of weak links is somewhat analogous to The Structure Hole (Structure Hole) theory, which was proposed by Ronald Burt in The document The Social Structure of Competition, where some positions in The network of interpersonal relationships are important, similar to broker (broker) positions, which, if nobody is present, form a Hole (Hole), which studies how individuals strive for greater benefit by filling The Structure Hole position for themselves. An article "Mining Structural Hole Networks Through Information Diffusion in Social Networks" published by Tianche Long and Jie Tang on WWW' 13 proposes two models of HIS and MaxD, and mines Structural Hole connections (Structural Hole Networks) in a large-scale Social network on top of the two models they propose. In the research of Joint Community and Structural House screw Detection via Harmonic modulation, Lifang He, Chun-Ta Lu et al proposed a Harmonic module method, which can solve two problems simultaneously.
The theory presented above, or both, explains descriptively what are weakly connected nodes (or structure hole nodes), e.g., Mark grandvetter and Ronald Burt et al do not suggest how to quickly find weakly connected nodes in a network. Or aiming at the general social networks with the traditional structures, such as the Tiancheng Long research group and the Lifang He research group, different models or algorithms are provided, but the algorithms have better effect when being used in the traditional social networks, and the network structure has the characteristics of multi-center and short path because the publishers and the receivers of the information of the new media platform network have more interaction. The special network architecture features of the new media platform are not particularly practical.
The new media platform is developed vigorously and is prosperous, and obtaining information through the new media platform has become a daily habit of many people. Meanwhile, it is also seen that the rumors and illegal messages on these platforms are many, and somebody tentatively says that the friend circle has become a "rumor circle", and the quick-hand shaking platform frequently explodes various illegal live broadcasts, but the unreasonable phenomena are not solved once. The blocking is not easy, the simple blocking is an ear-covering and bell-stealing phenomenon, meanwhile, a great deal of positive energy information is also seen on various new media platforms, for example, on-line live broadcast with most defects, sports live broadcast, lecture live broadcast and large coffee lecture live broadcast are also seen on the media platforms, and a great number of live broadcast teachers turn to be 'net red', which is very helpful for many people to obtain information. Therefore, the network rumors and illegal information need to be precisely managed. In addition, good products need good marketing, "wine aroma is afraid of roadway depth", a new media platform is a good platform for marketing, and live broadcast delivery becomes a new shopping diversion trend. Therefore, if the new media platform is effectively utilized, the information dissemination on the new media platform is guided, including the control on rumors and the marketing of products, so that the new media platform has obvious social and economic benefits.
The weak connection node plays a role of 'gate' for information propagation, the gate is closed, rumors are difficult to continue to diffuse, and the information can be propagated to new people by opening the gate.
Due to the characteristics of multiple centers and short path of the network structure of the new media platform, a heuristic acquisition method for propagating weak connection nodes based on network information of the new media platform is urgently needed to be provided.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a method for acquiring a new media platform network information transmission weak connection node.
In order to achieve the purpose, the invention is implemented according to the following technical scheme:
a method for acquiring a new media platform network information propagation weak connection node comprises the following steps:
s1, constructing a propagation network graph structure according to the new media platform network;
s2, constructing an adjacency matrix of the graph according to the graph structure of the propagation network;
s3, defining an objective function of the weak connection node according to the characteristics of the weak connection structure;
s4, constructing a degree matrix of the graph according to the graph structure of the propagation network, generating a corresponding Laplacian matrix of the graph according to the adjacency matrix of the graph and the degree matrix of the graph, and then calculating the Fielder eigenvector of the Laplacian matrix of the graph;
and S5, analyzing the Fielder eigenvectors, locating the weak connection nodes in the middle part of the Fielder eigenvectors of the Laplacian matrix of the graph, and obtaining the weak connection nodes of the propagation network by using a heuristic algorithm.
Further, the S1 specifically includes:
and (3) capturing corresponding data by using data released by the new media platform or using a web crawler, wherein the relationship between the attention and the attention on the new media platform forms a propagation network.
Further, the objective function of the weak link node defined in S3 is:
wherein, | S | and
respectively, the number of nodes in two subsets of weakly connected nodes, and E (i, j) the number of edges connecting i-node to j-node, where i-node belongs to S and j-node to
Further, in S4, the laplace matrix of the graph is L, and L ═ D-a is satisfied, where: d is the degree matrix of the graph, and A is the adjacency matrix of the graph.
Further, in S5, the heuristic algorithm specifically includes:
s51, setting a weak connection node number threshold epsilon;
s52, calculating a critical node block;
s53, dividing the propagation network graph structure into two parts based on the critical node blocks, and repeating S52 in the two parts respectively until reaching the limit of the threshold epsilon.
Compared with the prior art, the invention firstly constructs a propagation network graph structure according to the new media platform network; constructing an adjacency matrix of the graph according to the graph structure of the propagation network; defining an objective function of the weak connection node according to the characteristics of the weak connection structure; constructing a degree matrix of the graph according to the graph structure of the propagation network, generating a corresponding Laplacian matrix of the graph according to the adjacency matrix of the graph and the degree matrix of the graph, and then calculating a Fielder eigenvector of the Laplacian matrix of the graph; and analyzing the Fielder eigenvector, positioning the weak connection node in the middle part of the Fielder eigenvector of the Laplace matrix of the graph, and obtaining the weak connection node of the propagation network by using a heuristic algorithm. The method has a certain application prospect for finding the weak connection node in the new media platform propagation network, no matter the rumor is managed and controlled or the product is marketed.
Drawings
Fig. 1 is a diagram of a propagation network in the prior art.
Fig. 2 is a propagation network on the twitter platform.
Fig. 3 is a adjacency matrix corresponding to the propagation network diagram structure of fig. 1.
Fig. 4 is an example of a weak link node in the prior art.
Fig. 5 is a degree matrix corresponding to the propagation network diagram structure of fig. 1.
Fig. 6 is a laplacian matrix corresponding to the propagation network diagram structure of fig. 1.
Fig. 7 is an eigenvector of the laplacian matrix corresponding to the propagation network map structure of fig. 1.
Fig. 8 is a propagation network diagram structure of eigenvectors labeled with laplace matrix.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. The specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
The embodiment discloses a method for acquiring a new media platform network information propagation weak link node, which comprises the steps of firstly constructing a propagation network graph structure according to a new media platform network; constructing an adjacency matrix of the graph according to the graph structure of the propagation network; defining an objective function of the weak connection node according to the characteristics of the weak connection structure; constructing a degree matrix of the graph according to the graph structure of the propagation network, generating a corresponding Laplacian matrix of the graph according to the adjacency matrix of the graph and the degree matrix of the graph, and then calculating a Fielder eigenvector of the Laplacian matrix of the graph; and analyzing the Fielder eigenvector, positioning the weak connection node in the middle part of the Fielder eigenvector of the Laplace matrix of the graph, and obtaining the weak connection node of the propagation network by using a heuristic algorithm. The following is specifically set forth:
Data issued by the new media platform (some new media platforms can issue anonymized data periodically for research use by researchers or other personnel), or corresponding data is captured by a web crawler (an internet tool can capture data content on the network), and a relationship between attention and the concerned attention on the new media platform can form a propagation network. As shown in fig. 2, which is a propagation network on the twitter platform.
The adjacency matrix is a term in computer theory, and is an n × n matrix, and in a graph structure, if there is a connecting edge between two nodes, the position of the corresponding matrix is 1, otherwise it is 0. Taking fig. 1 as an example, fig. 3 shows an array of adjacent moments (denoted as matrix a) corresponding to fig. 1.
In the prior art, it is mentioned that Mark grandvetter et al, although proposing weak connections (or similar concepts), formally describe what are weak connection nodes. This embodiment gives a mathematical description of weakly connected nodes.
The weakly connected node set should partition a graph into two unconnected and balanced subgraphs S and S at a small cost, intuitively, the number of weakly connected node sets should be as small as possible, but it is merely desirable to minimize the number of weakly connected nodes, with the likely result that a single point is partitioned from other nodes in the graph, as shown in fig. 4, and the graph should in fact be "balanced" apart, i.e., any part of the partitioned graph should be "large enough". For example, the e node in fig. 4 is a "good" weakly connected node, and the i node in the figure is a "bad" weakly connected node. Thus, the objective function for weakly connected nodes is:
wherein, | S | and
respectively, the number of nodes in two subsets of weakly connected nodes, and E (i, j) the number of edges connecting i-node to j-node, where i-node belongs to S and j-node to
Step 4, calculating Laplace matrix of graph
The degree of a node is represented as the number of nodes directly connected to the node. It is also possible to define a degree matrix of the graph, which is a diagonal matrix, i.e. the elements have values only in the diagonal, and the other elements are all 0, the corresponding degree matrix D of fig. 1 being shown in fig. 5.
After the adjacency matrix and the degree matrix of the graph are obtained, the laplacian matrix L-D-a of one graph is defined, and thus the laplacian matrix corresponding to fig. 1 is shown in fig. 6.
It is proved by theory that the weak connection node is located in the middle of the eigenvector (second small eigenvector for short, mathematically also called the Fiedler vector) corresponding to the second small eigenvalue corresponding to the laplacian matrix.
The 10 vectors shown in fig. 7 are eigenvectors of the laplacian matrix corresponding to fig. 1, where the v2 vector is a Fiedler vector. (two decimals are reserved, 0.00 represents 0 due to computational accuracy and rounding).
The values of vector v2 are labeled onto the corresponding structure of FIG. 1, as shown in FIG. 8.
Step 5, analyzing the Fielder eigenvector, locating the weak connection node in the middle part of the Fielder eigenvector of the Laplace matrix of the graph, and obtaining the weak connection node of the propagation network by utilizing a heuristic algorithm
A heuristic algorithm uses the difference (gap) between the numbers in the Fiedler vector to find the corresponding propagating weakly connected nodes. The method specifically comprises the following steps:
1) setting a number threshold epsilon of weak connection nodes;
2) calculating a critical node block;
3) the propagation network graph structure is divided into two parts on the basis of critical node blocks, and step 2) is repeated in each of the two parts until the limit of the threshold epsilon is reached.
Specifically, in the present embodiment, as shown in fig. 8, for the numbers in the Fiedler vector (i.e., the numbers in the v2 vector, the present example has ten nodes in total, so there should be ten numbers, which are denoted as n1, n2, … …, and n10), and gi ═ n is calculatedi-ni-1(i>2 and i<N) forming a sequence g, gi representing the "gap" (gap) between two adjacent vectors, and finding the node according to the larger value of gi. In this embodiment, n4-n3 are the largest (corresponding to c node and d node, respectively), so node d is the weak link node. In some networks, there may be more than one weakly connected node, meaning that multiple nodes segment a network, and if the resulting gap results are equal, then all of these nodes may be weakly connected nodes.
In summary, the method has a certain application prospect for finding the weak connection node in the new media platform propagation network, no matter the rumor is managed and controlled or the product is marketed.
The technical solution of the present invention is not limited to the limitations of the above specific embodiments, and all technical modifications made according to the technical solution of the present invention fall within the protection scope of the present invention.
Claims (5)
1. A method for acquiring a new media platform network information propagation weak connection node is characterized by comprising the following steps:
s1, constructing a propagation network graph structure according to the new media platform network;
s2, constructing an adjacency matrix of the graph according to the graph structure of the propagation network;
s3, defining an objective function of the weak connection node according to the characteristics of the weak connection structure;
s4, constructing a degree matrix of the graph according to the graph structure of the propagation network, generating a corresponding Laplacian matrix of the graph according to the adjacency matrix of the graph and the degree matrix of the graph, and then calculating the Fielder eigenvector of the Laplacian matrix of the graph;
and S5, analyzing the Fielder eigenvectors, locating the weak connection nodes in the middle part of the Fielder eigenvectors of the Laplacian matrix of the graph, and obtaining the weak connection nodes of the propagation network by using a heuristic algorithm.
2. The method for acquiring a new media platform network information dissemination weak link node as claimed in claim 1, wherein the step S1 specifically comprises:
and (3) capturing corresponding data by using data released by the new media platform or using a web crawler, wherein the relationship between the attention and the attention on the new media platform forms a propagation network.
3. The method for acquiring new media platform network information dissemination weak link node as claimed in claim 1, wherein the objective function of the weak link node defined in S3 is:
wherein, | S | and
respectively, the number of nodes in two subsets of weakly connected nodes, and E (i, j) the number of edges connecting i-node to j-node, where i-node belongs to S and j-node to
4. The method for acquiring new media platform network information dissemination weak link nodes according to claim 1, wherein in S4, the laplacian matrix of the graph is L and L ═ D-a is satisfied, where: d is the degree matrix of the graph, and A is the adjacency matrix of the graph.
5. The method for acquiring network information dissemination weak link nodes of a new media platform as claimed in claim 1, wherein in S5, the heuristic algorithm comprises the specific steps of:
s51, setting a weak connection node number threshold epsilon;
s52, calculating a critical node block;
s53, dividing the propagation network graph structure into two parts based on the critical node blocks, and repeating S52 in the two parts respectively until reaching the limit of the threshold epsilon.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113742604A (en) * | 2021-08-24 | 2021-12-03 | 三峡大学 | Rumor detection method and device, electronic equipment and storage medium |
| CN117255226A (en) * | 2023-09-04 | 2023-12-19 | 北京工商大学 | Method and system for predicting cross-platform propagation range of live E-commerce information |
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| CN110825948A (en) * | 2019-11-05 | 2020-02-21 | 重庆邮电大学 | Rumor propagation control method based on rumor-splitting message and representation learning |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113742604A (en) * | 2021-08-24 | 2021-12-03 | 三峡大学 | Rumor detection method and device, electronic equipment and storage medium |
| CN113742604B (en) * | 2021-08-24 | 2024-04-16 | 三峡大学 | Rumor detection method and device, electronic equipment and storage medium |
| CN117255226A (en) * | 2023-09-04 | 2023-12-19 | 北京工商大学 | Method and system for predicting cross-platform propagation range of live E-commerce information |
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