Authors:
Jaya Sreevalsan-Nair
1
and
Astha Jakher
2
Affiliations:
1
Graphics-Visualization-Computing Lab, International Institute of Information Technology Bangalore, Bangalore, India
;
2
Department of Humanities and Social Sciences, IIT Kharagpur, Kharagpur, West Bengal 721302, India
Keyword(s):
Real-world Graphs, Directed Networks, Edge Sparsity, Dynamic Networks, Community Detection, Community Evaluation, Migration Flows, Co-association, Prediction, Autoregressive Models, VAR Model, ARMA Model.
Abstract:
Predicting the community structure in the time series, or snapshots, of a real-world graph in the future, is a pertinent challenge. This is motivated by the study of migration flow networks. The dataset is characterized by edge sparsity due to the inconsistent availability of data. Thus, we generalize the problem to predicting community structure in a dynamic sparse directed network (DSDN). We introduce a novel application of co-association which is a pairwise relationship between the nodes belonging to the same community. We thus propose a three-step algorithm, CAP-DSDN, for co-association prediction (CAP) in such a network. Given the absence of benchmark data or ground truth, we use an ensemble of community detection (CD) algorithms and evaluation metrics widely used for directed networks. We then define a metric based on entropy rate as a threshold to filter the network for determining a significant and data-complete subnetwork. We propose the use of autoregressive models for pred
icting the co-association relationship given in its matrix format. We demonstrate the effectiveness of our proposed method in a case study of international refugee migration during 2000–18. Our results show that our method works effectively for migration flow networks for short-term prediction and when the data is complete across all snapshots.
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