About our works

There has been a variety of research work in recent years, including research on community detection, link prediction, bipartite networks, recommendation algorithms and so on. The concept of heterogeneous networks has gradually attracted the attention of the academic community.

  • 1

    The community structure is a subnet in the network. These subgraphs are closely connected internally and the external connections are sparse. In 2002, Girvan and Newman revealed the structural characteristics of the community in the network, and opened up a boom in community structure research that continues to this day. The research team has published more than 40 related papers at domestic and international journal conferences. Aiming at the resolution problem of modularity, the research team improved the module density, proposed a variable resolution heuristic community partitioning algorithm [1], and modeled the novel Three Kingdoms network, detecting the community structure and reality. The situation is consistent, verifying the effectiveness of the algorithm.

    [1]. Dongming Chen, Yanlin Dong, Dongqi Wang and Xinyu Huang.A community finding method for weighted dynamic online social network based on user behavior, International Journal of Distributed Sensor Networks
  • 2

    What really plays an important role in the network is a small number of key nodes, and there is no unified benchmark for trusted services. At present, the indicators used to evaluate the importance of nodes in complex networks mainly include node degrees, medians, proximity centers, feature vector centers, K-cores, H-indexes, and so on. In the study of the hub node of the Shenyang public transport network, the research team summarized the existing bus network model, fully considered the passenger transfer time and the transfer station, and found that Shenyang Station, Huangsi Square Station and Shenyang North Station played in the city bus system.

    [1]. Dongming Chen, Xinyu Huang, Dongqi Wang, Lulu Jia.Public Transit Hubs Identification Based On Complex Networks Theory, IETE Technical Review.
    [2]. Dong-ming CHEN, Dong-fang SIMA, Xin-yu HUANG. Overlapping community and overlapping node discovery algorithm based on edge similarity, ICEIT2017.
  • 3

    The concept of heterogeneous networks has gradually attracted the attention of the academic community. As a special heterogeneous network, the binary network exists widely in life. Compared with traditional collaborative filtering algorithms, the recommended results are more accurate and more efficient. In view of the dynamic changes of the network, the academic community has formed the concept of a temporal network, and proposed a temporal graph model and a time series graph model. The Multilayer Network model includes multi-relational networks, multi-level networks, multi-mode networks, etc., which can portray the network in more detail, and is also a hot issue in network science research.

    [1]. Dongming Chen, Wei Zhao, Dongqi Wang, Xinyu Huang. Similarity-based Local Community Detection for Bipartite Networks, Filomat.
    [2]. Dongming Chen, Yanbin Yan, Dongqi Wang, Xinyu Huang.Community Detection Algorithm Based on Structural Similarity for Bipartite Networks, 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS 2016)