On synthetic data generation for anomaly detection in complex social networks

• URL: http://arxiv.org/abs/2010.13026v1
• Date: Sun, 25 Oct 2020 03:53:19 GMT
• Title: On synthetic data generation for anomaly detection in complex social networks
• Authors: Andreea Sistrunk, Vanessa Cedeno and Subhodip Biswas
• Abstract summary: This paper studies the feasibility of synthetic data generation for mission-critical applications. In particular, the development of a generative model, capable of creating data for anomalous rare activities in complex social networks is sought.
• Score: 1.1602089225841632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper studies the feasibility of synthetic data generation for mission-critical applications. The emphasis is on synthetic data generation for anomalous detection in complex social networks. In particular, the development of a heuristic generative model, capable of creating data for anomalous rare activities in complex social networks is sought. To this end, lessons from social and political literature are applied to prototype a novel implementation of the Agent-based Modeling (ABM) framework, based on simple social interactions between agents, for synthetic data generation in the context of terrorist profile desegregation. The conclusion offers directions for further verification, fine-tuning, and proposes future directions of work for the ABM prototype, as a complex-societal approach to synthetic data generation, by identifying heuristic hyper-parameter tuning methodologies to further ensure the generated data distribution is similar to the true distribution of the original data-sets. While a rigorous mathematical optimization for reducing the distances in distributions is not offered in this work, we opine that this prototype of an autonomous-agent generative complex social model is useful for studying and researching on pattern of life and anomaly detection where there is strict limitation or lack of sufficient data for using practical machine learning solutions in mission-critical applications.

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