Based on the social network analysis methods and human network, this paper randomly selected 44 students (31 males and 13 females) as the research objects, and it used the UCINET software to analyze the friendship between them of which 43 used WeChat and 44 used QQ, and it also used the tool Netdraw to visualize the network sociogram. By mining the four aspects of density, accessibility, centrality, block model, the results demonstrated that QQ social network and WeChat social network existed the phenomenon of small world, leaders and subgroups, and the key nodes of QQ human network were more than WeChat network. Through using the key nodes, it can push the precise and efficient information and improve the accuracy of information transmission and impact among network members.
With the development of Web 2.0 and the increasement of using self-media, people can obtain the accurate and effective information by excavating the characteristics and phenomena of social network. Many definitions of the human network have been developed. The social systems which we live with (families, schools, friendship groups, and so on) can be described as networks and analyzed using social network analysis [
At present, the research of social network mainly focuses on the enterprise competitive intelligence. Bao [
This paper randomly selects 44 students of certain specialty, and constructs friends adjacency matrix by EXCEL based on the relationship of QQ and WeChat. It uses Ucinet to analyze the social network and Netdraw to visualize the human network sociogram. By computing the Network density, reachability, centrality and block model, it can obtain the small world phenomena of the network, few key nodes and subgroups (small groups) with highest nodes degree dominating the whole network. Through analyzing the key nodes and the information concerned by small groups, it can improve the efficiency of information pushing and avoid the phenomenon of information overloading.
This study randomly selected 44 students (31 males and 13 females) as the research objects, and it investigated the friendship between them of which 43 used WeChat and 44 used QQ.
The social network refers to the network with complex connection relations, which formed by the social individual as nodes and the relations between the individuals as edges [
The basic elements of human network include persons and the links between persons. The former can be called nodes and the latter can be called relations or ties [
Ucinet (University of California at Irvine Network) is a comprehensive social network analysis software developed by University of Cingifornia Irvine [
In this paper, the analysis of social network uses the Ucinet 6.232 version to build two-dimensional adjacency matrix and process network data. The network sociogram is built by the NetDraw 2.118 version.
In order to facilitate recording, this paper used numerical number instead of stu- dent’s name. The relationship between the participants of the study forms a 44 × 44 two-dimensional adjacency matrix. If they are friends of each other, the corresponding element value is 1 and otherwise is 0. The results are shown in
01 | 02 | 03 | 04 | … | 43 | 44 | |
---|---|---|---|---|---|---|---|
01 | 0 | 1 | 1 | 1 | … | 1 | 1 |
02 | 1 | 0 | 1 | 1 | … | 1 | 1 |
03 | 1 | 1 | 0 | 1 | … | 1 | 1 |
04 | 1 | 1 | 1 | 0 | … | 0 | 1 |
… | … | … | … | … | … | … | … |
43 | 1 | 1 | 1 | 0 | … | 0 | 1 |
44 | 1 | 1 | 1 | 1 | … | 1 | 0 |
01 | 02 | 03 | 04 | … | 43 | 44 | |
---|---|---|---|---|---|---|---|
01 | 0 | 1 | 1 | 1 | … | 1 | 0 |
02 | 1 | 0 | 1 | 1 | … | 1 | 1 |
03 | 1 | 1 | 0 | 1 | … | 0 | 1 |
04 | 1 | 1 | 1 | 0 | … | 0 | 1 |
… | … | … | … | … | … | … | … |
43 | 1 | 1 | 0 | 0 | … | 0 | 1 |
44 | 0 | 1 | 1 | 1 | … | 1 | 0 |
Netdraw can draw the human network sociogram of QQ and WeChat between students (shown as
This paper constructed the undirected and unweighted network. As shown in
study constitute a node set. If every two students are friends of each other, there is a link that represents their relations. The node 18 of
Network density is the most common social network analysis indicator, which reflects the close degree of associations between points [
In the social network, the greater of the network density, the closer connection of the network members, the higher frequency of interactions between members, and it is more conducive to disseminate and share the knowledge. The greater of the whole network density, the greater the impact on member internal behavior, attitudes, and it can obtain better teamwork. Coleman thought that the higher the degree of interaction between members, which leaded to a more positive impact on the group operation [
The function “network-cohesion-distance” in Ucinet can analyze the network accessibility, which can verify whether the network is a small world or not [
Network Density | 0.8578 | 0.3552 |
Standard Deviation | 0.3492 | 0.4786 |
Average Distance among Nodes | 1.142 | 1.652 |
Cohesiveness Index | 0.929 | 0.639 |
two human networks indicate the better cohesion among the human network members, which promote the information dissemination and sharing among human network members, and it also promotes the accurate information pushing in. human network.
Centricity is one of the important contents of social network analysis, and it is an important index of measuring rights or central position. The central position individual of social network has strong influence on others and owns a high social prestige. Degree centrality, closeness centrality and betweenness centrality are the three most common forms to describe the network centricity.
Degree centrality refers to the number of connections between some node and other nodes in the network [
Node indegree is the degree to which one node is concerned by other nodes. Node outdegree is the degree to which one node pays attention to other nodes. The nodes with higher node indegree indicate that they are followed by other
nodes. The nodes with higher node outdegree indicate that they should pay attention to other nodes. One node with higher node indegree and node outdegree menas that it is located in the center of the human network and they have more power and greate impacts on the small groups of information dissemination and exchanging [
Betweenness centrality refers to the times of a node lying on the shortest path of any other two nodes [
As shown in
The network centralization index of QQ is 0.28% and that of WeChat is 7.21%. The lower value indicates that the most nodes in the network can get information without other nodes as an intermediary [
Closeness centrality is different to the degree centrality and betweenness centrality. It refers to the extent of the node not controlled by other nodes. The smaller value illustrates that the node is in the core position of the human network, and it is not easily controlled by other nodes on the process of information dissemination [
information dissemination [
From
Degree centrality shows that the nodes 1, 14, 15, 21, 26 are in the absolute core position of QQ human network. Betweenness centrality shows that the ability of the five nodes controlling other nodes is strong and Closeness centrality shows that the five nodes are difficultly controlled by other nodes. The five nodes grasp the information dissemination and communication of the whole network. The network information can be accurately pushed through them.
Degree centrality shows that the nodes 44, 30, 14 are in the absolute core position of WeChat human network. Betweenness centrality shows that the ability of the nodes 44, 29, 30, controlling other nodes are strong and Closeness centrality shows that the nodes 36, 44, 1, are difficultly be controlled by other nodes. The node 44 grasps the information dissemination and communication of the whole network. The network information can be accurately pushed through it.
Degree Centrality | Betweenness Centrality | Closeness Centrality |
---|---|---|
1 | 1 | 1 |
14 | 14 | 14 |
15 | 15 | 15 |
21 | 21 | 21 |
26 | 26 | 26 |
31 | 31 | 31 |
Degree Centrality | Betweenness Centrality | Closeness Centrality |
---|---|---|
44 | 44 | 36 |
30 | 29 | 44 |
14 | 30 | 1 |
Block model method can partition each point based on structural information and simplify the information. Block model method can classify the nodes using structural equivalence [
From
By constructing the human network sociogram, it can get the high impact nodes of the human network, which can be the opinion leaders because of the great ability to acquire the information resource. By analyzing the network density it can conclude that the members of QQ human network are communicating closely. The frequency of interaction among members of the network is high, which facilitates the dissemination and sharing the knowledge among members. But the WeChat is less tightly linked. Through analyzing the human network accessibility, it shows that the two human networks have a small world phenomenon, and the network has strong internal cohesion. By analyzing the network and its internal member nodes, it can push the precise information and improve
the frequency of interaction among network members.
Analyzing the degree centrality can get the central nodes of the human network, which have great power in the process of information transmission and great influence on the communication between the members of the human network [
By analyzing the nodes 1, 14, 15, 21, 26, 31 of QQ human network and the node 44 of WeChat, it can find that these nodes occupy the most important position in the entire interpersonal network and master the trends of information dissemination and communication. It can improve the accuracy of information transmission of the whole network.
The analysis of block model can caculate the number of interpersonal subgroups and the closeness degree within the group members. Mining the common concerned information of each group can improve the accuracy and efficiency of information pushing.
This paper analyzes the whole human network, human network centricity and block model of the QQ human network and WeChat human network. It analyzes the characteristics of the human network from multiple measurement dimensions. By analyzing the whole human network, it can obtain the higher impact nodes, opinion leaders and it finds that the two human networks emerge the small word phenomenon. These nodes can push the precise information and improve the frequency of interaction among network members. By analyzing the human network centricity, it obtains the central nodes. These nodes occupy the most important position in the entire interpersonal network. They master the trends of information dissemination and communication. They can improve the accuracy of information transmission of the whole network. By analyzing the block model, it can obtain the subgroups of human network and the closeness degree within the group members. It can improve the efficiency of information pushing, and frequency of information sharing through each subgroup. We hope to provide a new direction for the research of human network precise information pushing.
This paper is supported by the National Social Science Foundation of China (Grant No. 14CTQ022).
Yang, M.J., Wang, Y. and Hou, X.R. (2017) Research on Accurate Information Pushing Based on Human Network. Social Networking, 6, 181- 196. https://doi.org/10.4236/sn.2017.62011