Xiaoming Li
Tianjin University
20 Papers
24 Citations
Xiaoming Li is an academic researcher from Tianjin University. The author has contributed to research in topics: Computer science & Node (networking). The author has an hindex of 7, co-authored 20 publications.
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Papers
Using Sparse Representation to Detect Anomalies in Complex WSNs
Xiaoming Li,Guangquan Xu,Xi Zheng,Kaitai Liang,Emmanouil Panaousis,Tao Li,Wei Wang,Chao Shen +7 more
TL;DR: A dictionary learning algorithm based on a non-negative constraint is developed, and a sparse representation anomaly node detection method for sensor networks is proposed based on the dictionary learning and verified the robustness of the proposed method in detecting abnormal nodes against four state of the art approaches.
34
Community detection for multi-layer social network based on local random walk
TL;DR: This work proposes a community detection algorithm for multi-layer social network based on local random walk (MRLCD), which can autonomously explore the local community structure of given node, and effectively improve the stability and accuracy for local community detection in multi- layer social network.
26
Big Data Analytics and Deep Learning in Bioinformatics With Hadoop
Sandhya Armoogum,Xiaoming Li +1 more
- 01 Jan 2019
TL;DR: In this chapter, big data analytics with regards to the Hadoop big data framework for storing and processing big data is described in the context of bioinformatics and machine learning is an important approach for performing predictive and prescriptive analytics.
26
Exploring temporal community structure and constant evolutionary pattern hiding in dynamic networks
TL;DR: A new clustering method based on non-negative matrix factorization from a fully probabilistic perspective is proposed, to explore temporal and constant community structure as well as the importance of nodes in any type dynamic networks synchronously.
22
Multi-Layer Network Local Community Detection Based on Influence Relation
TL;DR: A local community detection model based on the influence relation of the multi-layer network is proposed by combining the direct influence relation and indirect influence relationof the network (IMLC).