Book ChapterDOI
Ranking individuals and groups by influence propagation
Pei Li,Jeffrey Xu Yu,Hongyan Liu,Jun He,Xiaoyong Du +4 more
- pp 407-419
TLDR
A new influence propagation model is proposed to describe the propagation of pre-defined importance over individual nodes and groups accompanied with random walk paths, and a new IPRank algorithm is proposed for ranking both individuals and groups.Abstract:
Ranking the centrality of a node within a graph is a fundamental problem in network analysis. Traditional centrality measures based on degree, betweenness, or closeness miss to capture the structural context of a node, which is caught by eigenvector centrality (EVC) measures. As a variant of EVC, PageRank is effective to model and measure the importance of web pages in the web graph, but it is problematic to apply it to other link-based ranking problems. In this paper, we propose a new influence propagation model to describe the propagation of pre-defined importance over individual nodes and groups accompanied with random walk paths, and we propose new IPRank algorithm for ranking both individuals and groups. We also allow users to define specific decay functions that provide flexibility to measure link-based centrality on different kinds of networks. We conducted testing using synthetic and real datasets, and experimental results show the effectiveness of our method.read more
Citations
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Proceedings ArticleDOI
Influence Maximization over Large-Scale Social Networks: A Bounded Linear Approach
TL;DR: A bounded linear approach for influence computation and influence maximization is provided and a quantitative metric, named Group-PageRank, is developed to quickly estimate the upper bound of the social influence based on this linear approach.
Journal ArticleDOI
An Influence Propagation View of PageRank
TL;DR: A linear social influence model is proposed and it is revealed that this model generalizes the PageRank-based authority computation by introducing some constraints, and an upper bound for identifying nodes with top authorities is provided.
Journal ArticleDOI
The academic social network
TL;DR: This paper defines a variety of ranking metrics on different entities—authors, publication venues, and institutions, and discusses the computation aspects of these metrics, and the similarity between different metrics.
Proceedings ArticleDOI
CAST: A Context-Aware Story-Teller for Streaming Social Content
TL;DR: This paper proposes CAST, which is a context-aware story-teller that discovers new stories from social streams and tracks their structural context on the fly to build a vein of stories.
Proceedings ArticleDOI
Linear Computation for Independent Social Influence
TL;DR: This paper describes the linear social influence model, and defines the independent influence under this model for eliminating the "mutual enrichment" between seed nodes, and finds that the influence of a set of nodes is actually the sum of their independent influence, and gives upper bounds for independent influence.
References
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Randomized Algorithms
TL;DR: This book introduces the basic concepts in the design and analysis of randomized algorithms and presents basic tools such as probability theory and probabilistic analysis that are frequently used in algorithmic applications.
Journal ArticleDOI
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TL;DR: In this paper, Factoring and weighting approaches to status scores and clique identification were proposed, and the results showed that the weighting approach is more accurate than the factoring approach.