Ranking Users in Social Networks with Higher-Order Structures
نویسندگان
چکیده
PageRank has been widely used to measure the authority or the influence of a user in social networks. However, conventional PageRank only makes use of edge-based relations, ignoring higher-order structures captured by motifs, subgraphs consisting of a small number of nodes in complex networks. In this paper, we propose a novel framework, motif-based PageRank (MPR), to incorporate higher-order structures into conventional PageRank computation. We conduct extensive experiments in three real-world networks, i.e., DBLP, Epinions, and Ciao, to show that MPR can significantly improve the effectiveness of PageRank for ranking users in social networks. In addition to numerical results, we also provide detailed analysis for MPR to show how and why incorporating higher-order information works better than PageRank in ranking users in social networks.
منابع مشابه
Learning to blend vitality rankings from heterogeneous social networks
Heterogeneous social network services, such as Facebook and Twitter, have emerged as popular, and often effective channels for Web users to capture updates from their friends. The explosion in popularity of these social network services, however, has created the problem of “information overload”. The problem is becoming more severe as more and more users have engaged in more than one social net...
متن کاملExtracting Social Structure from DarkWeb Forums
This paper explores various Social Network Analysis (SNA) techniques in order to identify a range of potentially ‘important’ members of Islamic Networks within Dark Web Forums. For this experiment, we conducted our investigation on five forums collected in previous work as part of the Dark Web Forum portal and built upon the tool support created in our previous research in order to visualise an...
متن کاملThe Role of Online Social Networks in Users' Everyday-Life Information Seeking
Background and Aim: Considering the increasing number of users who interact with online social networks, it can be inferred that these networks have become an essential part of users' lives and play different roles in their everyday life. Therefore, the present study aims to explore the role of these networks in users' everyday-life information seeking. Method: This research is an applied resea...
متن کاملSemantically Rich Recommendations in Social Networks for Sharing, Exchanging and Ranking Semantic Context
Recommender algorithms have been quite successfully employed in a variety of scenarios from filtering applications to recommendations of movies and books at Amazon.com. However, all these algorithms focus on single item recommendations and do not consider any more complex recommendation structures. This paper explores how semantically rich complex recommendation structures, represented as RDF g...
متن کاملTweet Ranking Based on Heterogeneous Networks
Ranking tweets is a fundamental task to make it easier to distill the vast amounts of information shared by users. In this paper, we explore the novel idea of ranking tweets on a topic using heterogeneous networks. We construct heterogeneous networks by harnessing cross-genre linkages between tweets and semantically-related web documents from formal genres, and inferring implicit links between ...
متن کامل