Social Networks Embedding Based on the Employment of Community Recognition and Latent Semantic Feature Extraction Approaches
Subject Areas : electrical and computer engineering
Mohadeseh Taherparvar
1
,
Fateme Ahmadi abkenari
2
*
,
Peyman bayat
3
1 - Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran
2 - Department of Computer Engineering, Payame Nour University, Tehran, Iran
3 - Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran
Keywords: BTM topic model, community detection, deep learning, network embedding, overlapping social networks, semantic features,
Abstract :
The purpose of embedding social networks, which has recently attracted a lot of attention, is to learn to display in small dimensions for each node in the network while maintaining the structure and characteristics of the network. In this paper, we propose the effect of identifying communities in different situations such as community detection during or before the process of random walking and also the effect of semantic textual information of each node on network embedding. Then two main frameworks have been proposed with community and context aware network embedding and community and semantic feature-oriented network embedding. In this paper, in community and context aware network embedding, the detection of communities before the random walk process, is performed through using the EdMot non-overlapping method and EgoNetSplitter overlapping method. However, in community and semantic feature-oriented network embedding, the recognition of communities during a random walk event is conducted using a Biterm topic model. In all the proposed methods, text analysis is examined and finally, the final display is performed using the Skip-Gram model in the network. Experiments have shown that the methods proposed in this paper work better than the superior network embedding methods such as Deepwalk, CARE, CONE, and COANE and have reached an accuracy of nearly 0.9 and better than other methods in terms of edge prediction criteria in the network.
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