Link Prediction in Complex Networks using Embedding Techniques and Similarity Measures
Sanjay Kumar1, Vipul Gupta2, Sudhanshu Shekhar Singh3

1Sanjay Kumar*, Department of Computer Science and Engineering Delhi Technological University, New Delhi, India.
2Vipul Gupta, Department of Computer Science and Engineering, Delhi Technological University, New Delhi, India.
3Sudhanshu Shekhar Singh, Department of Computer Science and Engineering, Delhi Technological University, New Delhi, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 10, 2020. | PP: 1690-1696 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2762039520/2020©BEIESP | DOI: 10.35940/ijitee.E2762.039520
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: Networks have proved to be very helpful in modelling complex systems with interacting components. There are various problems across various domains where the systems can be modelled in the form of a network with links between interacting components. The Problem of Link Prediction deals with predicting missing links in a given network. The application of link prediction ranges across various disciplines including biological networks, transportation networks, social networks, telecommunication networks, etc. In this paper, we use node embedding methods to encode the nodes into low dimensional embeddings and predict links based on the edge embeddings computed by taking the hadamard product of the participating nodes. We further compare the accuracy of the models trained on different dimensions of embeddings. We also study how the introduction of additional features changes the accuracy when introduced to various dimensions of node embeddings. The additional features include overlapping measures such as Jaccard similarity, Adamic-Adar score and dot product between node embeddings as well as heuristic features i.e. Common Neighbors, Resource Allocation, preferential attachment and friend tns score. 
Keywords: Complex Networks, Network Embedding, Link Prediction, Online Social Networks, Similarity Measures.
Scope of the Article: Link Technologies