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Peixiang Zhao
Researcher at Florida State University
Publications - 39
Citations - 2507
Peixiang Zhao is an academic researcher from Florida State University. The author has contributed to research in topics: Graph (abstract data type) & Graph database. The author has an hindex of 19, co-authored 37 publications receiving 2203 citations. Previous affiliations of Peixiang Zhao include University of Illinois at Urbana–Champaign & Microsoft.
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Proceedings ArticleDOI
RankClus: integrating clustering with ranking for heterogeneous information network analysis
TL;DR: This paper addresses the problem of generating clusters for a specified type of objects, as well as ranking information for all types of objects based on these clusters in a multi-typed information network, and proposes a novel clustering framework called RankClus that directly generates clusters integrated with ranking.
Journal ArticleDOI
On graph query optimization in large networks
Peixiang Zhao,Jiawei Han +1 more
TL;DR: The experimental studies demonstrate the effectiveness and scalability of SPath, which proves to be a more practical and efficient indexing method in addressing graph queries on large networks.
Proceedings ArticleDOI
Evaluating event credibility on twitter
TL;DR: A credibility analysis approach enhanced with event graph-based optimization to solve the problem of automatically assessing the credibility of popular Twitter events and shows that its methods are significantly more accurate than the decision tree classifier approach.
Proceedings Article
Graph indexing: tree + delta <= graph
TL;DR: This study verifies that (Tree+Δ) is a better choice than graph for indexing purpose, denoted (Tree-Δ ≥Graph), to address the graph containment query problem and achieves an order of magnitude better performance in index construction.
Proceedings ArticleDOI
P-Rank: a comprehensive structural similarity measure over information networks
TL;DR: A new similarity measure, P-Rank (Penetrating Rank), toward effectively computing the structural similarities of entities in real information networks and a fixed point algorithm to reinforce structural similarity of vertex pairs beyond the localized neighborhood scope toward the entire information network is proposed.