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Yi Wang
Researcher at Beijing University of Posts and Telecommunications
Publications - 19
Citations - 882
Yi Wang is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Complex network & Adjacency list. The author has an hindex of 10, co-authored 19 publications receiving 772 citations. Previous affiliations of Yi Wang include Sprint Corporation & University of California, Riverside.
Papers
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Proceedings ArticleDOI
Execution Anomaly Detection in Distributed Systems through Unstructured Log Analysis
TL;DR: This paper proposes an unstructured log analysis technique for anomalies detection and proposes a novel algorithm to convert free form text messages in log files to log keys without heavily relying on application specific knowledge.
Journal ArticleDOI
Mining dependency in distributed systems through unstructured logs analysis
TL;DR: An approach to mine intercomponent dependencies from unstructured logs that requires neither additional system instrumentation nor any application specific knowledge and successfully identifies the dependencies among the distributed system components.
Journal ArticleDOI
A genetic algorithm for detecting communities in large-scale complex networks
TL;DR: A genetic algorithm with a special encoding schema for community detection in complex networks, which employs a metric, named modularity Q as the fitness function and applies a special locus-based adjacency encoding schema to represent the community partitions.
Proceedings ArticleDOI
Overlapping Community Detection in Bipartite Networks
TL;DR: In this paper, a novel algorithm BiTector (Bi-community DeTector) is proposed to mine the overlapping communities in large-scale sparse bipartite networks. But the algorithm is not suitable for large scale networks and it cannot identify the overlapping community structures efficiently and effectively.
Book ChapterDOI
A New Genetic Algorithm for Community Detection
TL;DR: A new genetic algorithm for community detection is proposed that uses the fundamental measure criterion modularity Q as the fitness function and a special locus-based adjacency encoding scheme is applied to represent the community partition.