Abstract
Forecasting possible future relationships between people in a network requires a study of the evolution of their links. To capture network dynamics and temporal variations in link strengths between various types of nodes in a network, a dynamic weighted heterogeneous network is to be considered. Link strength prediction in such networks is still an open problem. Moreover, a study of variations in link strengths with respect to time has not yet been explored. The time granularity at which the weights of various links change remains to be delved into. To tackle these problems, we propose a neural network framework to predict dynamic variations in weighted heterogeneous social networks. Our link strength prediction model predicts future relationships between people, along with a measure of the strength of those relationships. The experimental results highlight the fact that link weights and dynamism greatly impact the performance of link strength prediction.
Highlights
Researchers have tried to understand the fundamental concepts underlying human relationships by analyzing social networks
Link prediction is looked upon as a key task in social network analysis and has applications in recommender systems, network inference, health care [4] and terror network analysis, to name a few
The features were stored in a matrix form separately, and the matrices for all the time intervals were given as input to the Autoregressive Integrated Moving Average (ARIMA) model to forecast the feature values for a future time interval
Summary
Researchers have tried to understand the fundamental concepts underlying human relationships by analyzing social networks. These studies have opened up a new avenue for predicting future relationships among entities as well. In the case of bibliographic networks, link prediction is used to recommend authors for a review of journals, or as keynote speakers at a conference, or suggest who will collaborate with whom in the future. Link prediction can help find variations in strengths between author-topic links to discover where the current topic interest of an author lies, learn who the best in a field is, identify reviewers for a paper, choose a keynote speaker for a particular topic, and gauge the affinity between two authors with respect to a venue
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