Research On Analyzing Influence Of Users In Social Network

Social Media becomes more and more popular with the development of Internet, which has characters of mass of users, evolutionary topics, dynamic network structures. Its shortcomings are also very clear: openness of network brings out low threthhold, difference of cognition brings out many angles of representing disapprovals, quickly spreading of information brings out uncontrollability of communication range, all these characters present both opportunities and challenges for the government management. It is important to identify influential or authoritative users in social media, because influential users produce more information and lead to a healthy opinion. Mining influential users to enlarge those information dissemination is important to help public stability and harmonious order. This dissertation takes BBS and Weibo as example, to study the key technologies of social influential mechanism in social network. The main work and contributions in this dissertation includes:(1) Social influence has social, network correlation, and dynamic features. First we analyzed key factors of influence, comprehensive influence analyzing on the basis of society and network, which are breadth, deepness and duration. Complex correlation of users in social network, interaction strength between users and deeply interaction by article’s formulation are behavior features of social activities, influential users analyzing algorithm based on multiple attributes. Correlation matrix is constructed by users’ reply and indirect relation is analyzed. User’s behavior is evaluated from local view. Based on the Pagerank algorithm, introducing user’s behavior feature and the user relational networks, this paper designs a multiple attributes rank(MAR) algorithm. Deficiency of Page Rank is that rank score increased just by link direction, which ignored the difference between link’s strength. We introduced personalized enforcement ranking mechanism, which improved closeness between influential users. Influential user’s evolution trend is analyzed by time span division and stability. We conducted experiments with data from Tianya BBS, and evaluated multi-facets of issues of identifying influential users. Conclusion is arrived that some influential users are active users in forum and influential users have real leading duty.(2) Nodes are more important when locating in special place of network, we studied algorithms of structure hole users identification and analyzing. Structure hole based on influential user identification: Structure hole is very important for in network, which has opportunity to connect different community. Community and structural hole are different part of global characters of network. Structure hole connect different communities with weak strength sparsely. It is NP-hard question to identify network community. We can find structure hole when we search community. Users located in structure hole played more important role than influential user in community. In this paper, we proposed algorithm to identify community and structure hole synchronously. With multi-level hierarchical partition mechanism, we proposed merging patterns to condense network graph with local clusters, identify communities on condensed network and identify precisely on uncoarsed network, which was more efficient to identify communities in large graphs. Based on internal correlation and external correlation, we modified HITS algorithm to compute connection score of inner-community and outer-community.(3) Large scale social netork brings forth great challenges on influence analyzing, we studied correlation of influence and the characteristics of network structure, proposed quickly influence analyzing algorithm based on degree distribution. Average ratio of 0-indegree is 86.16%, average ratio of 1-outdegree is 64.98% in TIANYA BBS dataset. Indgree and outdegree follow power law. To decrease time-space ratio of influence computing, this paper analyzed relational graph of users in network BBS and deviation distribution of degree. Based on Pagerank, users are divided into two sets, 0-indegree in set1, non 0-indegree in set2, edges are from set1 pointing to set2 or between set2. The nodes in set1 are divided by out degree, nodes are listed together when the out degree is same. The quickly sorting algorithm is presented by applying set division method and list structure. With set partition algorithm based on degree distribution, time-space complexity decreaced from O(V+E) to O(V’), V’ is set of non 0-indegree. Experimental results on TIANYA BBS dataset demonstrate that SD-Rank is more efficient than Page Rank and Colibri which computing non-redundant orthogonal basis.(4) Influence is dynamic with event evoving, users bring explicit and implicit correlation, and different influence strength with different stage of event. Users influenced others by means of topic, reply is explicit correlation, participation in common subjects brings imlicit correlation. This paper proposed evolutionary influence algorithm with event evolving. Global event and subevent were detected based on everal distance functions which proposed to measure implicit correlation in this paper. Hot events are detected by the way of co-currence words in subjects. Event is progressive with the changing of co-currence words in subjects. User communities exist in relavant subjects implicitly, complicate techniques designed to identify user communities. Information diffusion has two characters: correlation and evolution. In this paper, evolutionary event based user influence ranking algorithm is proposed. During process of event diffusion and evolution, differentiate difference in instant influence and deferring influence during the course of topic diffusing and evolving, for deferring influence decaying exponentially with time. Although topic influence and user influence fluctuate asynchronous, concurrent peak of topic influence and user influence means influence mutual enforcement. The comprehensive analysis of reasons above brings about computational model of evolving influence based on dynamic event. Recall and precision in this paper are higher than those with static analyzing in datasets of forum and weibo. For global correlation and peak concurrent of hot event with user influence are considered, the proposed algorithm was more effective than others.