DeepLink: A Novel Link Prediction Framework based on Deep Learning

by   Mohammad Mehdi Keikha, et al.

Recently, link prediction has attracted more attentions from various disciplines such as computer science, bioinformatics and economics. In this problem, unknown links between nodes are discovered based on numerous information such as network topology, profile information and user generated contents. Most of the previous researchers have focused on the structural features of the networks. While the recent researches indicate that contextual information can change the network topology. Although, there are number of valuable researches which combine structural and content information, but they face with the scalability issue due to feature engineering. Because, majority of the extracted features are obtained by a supervised or semi supervised algorithm. Moreover, the existing features are not general enough to indicate good performance on different networks with heterogeneous structures. Besides, most of the previous researches are presented for undirected and unweighted networks. In this paper, a novel link prediction framework called "DeepLink" is presented based on deep learning techniques. In contrast to the previous researches which fail to automatically extract best features for the link prediction, deep learning reduces the manual feature engineering. In this framework, both the structural and content information of the nodes are employed. The framework can use different structural feature vectors, which are prepared by various link prediction methods. It considers all proximity orders that are presented in a network during the structural feature learning. We have evaluated the performance of DeepLink on two real social network datasets including Telegram and irBlogs. On both datasets, the proposed framework outperforms several structural and hybrid approaches for link prediction problem.



page 1

page 2

page 3

page 4


Link Prediction in Multiplex Networks based on Interlayer Similarity

Some networked systems can be better modelled by multilayer structure wh...

E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction

Predicting the potential relations between nodes in networks, known as l...

Learning-based link prediction analysis for Facebook100 network

In social network science, Facebook is one of the most interesting and w...

Predicting kills in Game of Thrones using network properties

TV series such as HBO's most popular show Game of Thrones have seen a hi...

Weakly supervised collective feature learning from curated media

The current state-of-the-art in feature learning relies on the supervise...

Modeling the Evolution of Networks as Shrinking Structural Diversity

This article reviews and evaluates models of network evolution based on ...

Handling Class Imbalance in Link Prediction using Learning to Rank Techniques

We consider the link prediction problem in a partially observed network,...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.