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Detecting outlier patterns with query-based artificially generated searching conditions

- Yu, Shuo, Xia, Feng, Sun, Yuchen, Tang, Tao, Yan, Xiaoran, Lee, Ivan

  • Authors: Yu, Shuo , Xia, Feng , Sun, Yuchen , Tang, Tao , Yan, Xiaoran , Lee, Ivan
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Computational Social Systems Vol. 8, no. 1 (2021), p. 134-147
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  • Description: In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas, such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, and national security. However, subgraph matching remains a computationally challenging problem, let alone identifying special motifs among them. This is especially the case in large heterogeneous real-world networks. In this article, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way. Our method takes advantage of the semantics provided by a user's query, which in turn provides the mathematical constraint that is crucial for faster detection. We propose an approach to generate query conditions based on the user's query. In particular, we use meta paths between the nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time. The proposed method is examined in a real-world academic network using different similarity measures between the nodes. The experiment result demonstrates that our method can identify interesting motifs and is robust to the choice of similarity measures. © 2014 IEEE.

Disease gene prediction based on heterogeneous probabilistic hypergraph ranking

- Ding, Feng, Kong, Xiangjie, Zhao, Zhehuan, Xia, Feng, Liu, Anfu, Bai, Chenxu, Xu, Bo, Liu, Xiaoxia, Sang, Shengtian, Lin, Hongfei, Yang, Zhihao, Wang, Jian

  • Authors: Ding, Feng , Kong, Xiangjie , Zhao, Zhehuan , Xia, Feng , Liu, Anfu , Bai, Chenxu , Xu, Bo , Liu, Xiaoxia , Sang, Shengtian , Lin, Hongfei , Yang, Zhihao , Wang, Jian
  • Date: 2019
  • Type: Text , Conference proceedings
  • Relation: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); San Diego, CA, USA; 18-21 November 2019 p. 2021-2028
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  • Description: In order to save time and cost, many disease gene prediction methods have been proposed in recent years. However, the traditional network model uses a binary relationship to represent the relationship between different proteins or gene molecules and phenotypes, which leads to the loss of information. Recently, hypergraph shows that it can overcome this loss of information to some extent and preserve the multivariate relationship, so we transformed the disease gene prediction problem into the problem of ranking the multivariate-relationship object. In this paper, we propose a method of Heterogeneous Probabilistic Hypergraph Ranking (HPHR) to predict disease genes. Firstly, fix a graph centroid for each hyperedge and according to different associations, and add other nodes related to the graph centroid to hyperedges with a certain probability. Then transform the problem of predicting disease genes into the problem of ranking heterogeneous objects, and the candidate genes are sorted by hypergraph ranking. The method is then applied to the integrated disease gene network. Compared with other prediction methods achieved better results, which was verified by this experiment.
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