A. Torkaman; K. Badie; A. Salajegheh; M. H. Bokaei; Seyed F. Fatemi
Abstract
Recently, network representation has attracted many research works mostly concentrating on representing of nodes in a dense low-dimensional vector. There exist some network embedding methods focusing only on the node structure and some others considering the content information within the nodes. In this ...
Read More
Recently, network representation has attracted many research works mostly concentrating on representing of nodes in a dense low-dimensional vector. There exist some network embedding methods focusing only on the node structure and some others considering the content information within the nodes. In this paper, we propose HDNR; a hybrid deep network representation model, which uses a triplet deep neural network architecture that considers both the node structure and content information for network representation. In addition, the author's writing style is also considered as a significant feature in the node content information. Inspired by the application of deep learning in natural language processing, our model utilizes a deep random walk method to exploit inter-node structures and two deep sequence prediction methods to extract nodes' content information. The embedding vectors generated in this manner were shown to have the ability of boosting each other for learning optimal node representation, detecting more informative features and ultimately a better community detection. The experimental results confirm the effectiveness of this model for network representation compared to other baseline methods.
D. Data
M. Zarezade; E. Nourani; Asgarali Bouyer
Abstract
Community structure is vital to discover the important structures and potential property of complex networks. In recent years, the increasing quality of local community detection approaches has become a hot spot in the study of complex network due to the advantages of linear time complexity and applicable ...
Read More
Community structure is vital to discover the important structures and potential property of complex networks. In recent years, the increasing quality of local community detection approaches has become a hot spot in the study of complex network due to the advantages of linear time complexity and applicable for large-scale networks. However, there are many shortcomings in these methods such as instability, low accuracy, randomness, etc. The G-CN algorithm is one of local methods that uses the same label propagation as the LPA method, but unlike the LPA, only the labels of boundary nodes are updated at each iteration that reduces its execution time. However, it has resolution limit and low accuracy problem. To overcome these problems, this paper proposes an improved community detection method called SD-GCN which uses a hybrid node scoring and synchronous label updating of boundary nodes, along with disabling random label updating in initial updates. In the first phase, it updates the label of boundary nodes in a synchronous manner using the obtained score based on degree centrality and common neighbor measures. In addition, we defined a new method for merging communities in second phase which is faster than modularity-based methods. Extensive set of experiments are conducted to evaluate performance of the SD-GCN on small and large-scale real-world networks and artificial networks. These experiments verify significant improvement in the accuracy and stability of community detection approaches in parallel with shorter execution time in a linear time complexity.
