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A model complex network is a large system of elements (vertices)
that are joined by non-trivial relationships (edges). Examples of
such complex networks include the WWW, metabolic and protein
networks, social networks, and economic and financial markets. The
underlying principles and laws of these network systems can help us
construct more effective communication mechanisms, find cures for
fatal diseases, and deal with economic crises.
Building upon
an innovative blend of graph theoretical, information theoretical,
and statistical learning concepts and techniques, the proposed mining
methodologies in this project will 1) extract network backbones which
both simplify and highlight network structures, 2) measure the
network difference for comparative network analysis, and 3) apply
causal inference to integrate time series with network topology. In a
close collaboration with domain experts from bioinformatics,
political science, and software engineering, the proposed techniques
have the potential to help reveal the organizational principles of
biocellular systems in a dynamic environment; identify therapeutic or
drug targets; illuminate how large scale software systems form and
evolve; and understand how human society is organized at the
individual level (social networks) and organizational level
(political science). Using the popular online social networks, such
as MySpace and Facebook, as "hooks", this project will
attract, recruit, and prepare students from underrepresented groups
including women and minorities to computer science and involve
underrepresented students in the cutting-edge research.
Ruoming Jin (PI)
Ning Ruan (Ph.D. 2012, Google)
Lin Liu (Ph.D. candidate)
Yelong Shen (Ph.D. student)
Nicholas Tietz (Undergraduate Student)
Tim Fox (Undergraduate Student)
Henry Butler (Undergraduate student)
Lin Liu, Ruoming Jin, Charu C. Aggarwal, Yelong Shen: Reliable Clustering on Uncertain Graphs. ICDM 2012: 459-468
Yelong Shen, Ruoming Jin, Dejing Dou, Nafisa Afrin Chowdhury, Junfeng Sun, Brigitte Piniewski, David Kil: Socialized Gaussian Process Model for Human Behavior Prediction in a Health Social Network. ICDM 2012: 1110-1115
Yelong Shen, Ruoming Jin: Learning personal + social latent factor model for social recommendation. KDD 2012: 1303-1311
Ruoming Jin, Ning Ruan, Saikat Dey, Jeffrey Xu Yu: SCARAB: scaling reachability computation on large graphs. SIGMOD Conference 2012: 169-180
Ruoming Jin, Ning Ruan, Yang Xiang, Victor E. Lee: A highway-centric labeling approach for answering distance queries on large sparse graphs. SIGMOD Conference 2012: 445-456
Ruoming Jin, Victor E. Lee, Hui Hong: Axiomatic Ranking of Network Role Similarity, in KDD'11.
Ruoming Jin, Lin Liu, Charu C. Aggarwal, Discovering Highly Reliable Subgraphs in Uncertain Graphs, in KDD'11.
Ning Ruan, Ruoming Jin, and Yan Huang, Distance Preserving Graph Simplification, in ICDM'11.
Haishan Liu, Paea LePendu, Ruoming Jin, and Dejing Dou, A Hypergraph-based Method for Discovering Semantically Associated Itemsets, in ICDM'11.
Ruoming Jin, Lin Liu, Bolin Ding, and Haixun Wang, Distance-Constraint Realiability Computation in Uncertain Graphs, in VLDB'11.
Jun Gao, Jeffrey Xu Yu, Ruoming Jin, Jiashui Zhou, Tengjiao Wang, and Dongqiang Yang, Neighborhood-Privacy Protected Shortest Distance Computing in Cloud, in SIGMOD'11.
This material is based upon work supported by National Science Foundation under CAREER Award IIS-0953950. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. |