Workshop on Bridging Diverse Connections through Mobile Social Networking

Event Description

We attend in-person events but often forget who we met and where. We receive many contact requests in our online social networks like LinkedIn and Facebook from people we do not know. How can we make it easier for people to connect from offline to online? In this workshop, we will discuss:

• Cutting-edge research on connecting people through offline and online interactions and how technology facilitates these transitions.

• Strategies for incorporating diversity into the development of mobile social networking applications.

• Establish a multidisciplinary and diverse network in mobile social networking, involving experts from academia, industry, and Chicago's inclusive populations across computer science, sociology, and human-computer interaction.

This event will feature a Distinguished ACM Speaker, Anura Jayasumana, Professor, Electrical & Computer Engineering, and Computer Scienceat Colorado State University.

Talk title:

Emergent Patterns in Social Networks

Abstract:

Detecting latent or emerging patterns in dynamic networks is crucial across multiple domains, including homeland security, consumer analytics, behavioral health, and social computing. However, dynamic network data presents unique challenges in collection, mining, analytics and processing. We will present INSiGHT (Investigative Search for Graph Trajectories), a comprehensive framework for detecting emergent patterns of behaviors in knowledge networks containing social and behavioral data, with a focus on detection of domestic radicalization. To account for recurring behavioral indicators and the recency of behaviors as the imminence of a threat, e.g., INSiGHT provides parameterized methods to score multiple occurrences of indicators and to dampen the significance of indicators over time. Recognizing that radicalization can occur within small groups or collective plots, INSiGHT employs a non-combinatorial neighborhood matching technique that enables analysts to identify clusters of individuals potentially engaged in conspiracies. Our approach combines advanced natural language processing (NLP) and supervised machine learning models to classify textual data for radicalization indicators. Additionally, we have developed PINGS (Procedures for Investigative Graph Search), a specialized graph database library tailored for investigative search in social network mining. Finally, we will explore the potential of emerging Graph Neural Networks (GNNs) and Graph Embedded Neural Networks (GENNs) to pave the way for more advanced analytical capabilities in dynamic network analysis.

About the Speaker:

Anura P. Jayasumana is a Professor in Electrical & Computer Engineering at Colorado State University where he holds a joint appointment in Computer Science. He is the Director of the Information Science and Technology Center (ISTeC) at CSU, a university-wide organization for promoting research, teaching and service in information sciences and technologies. He received a Ph.D. and M.S. in Electrical Engineering from Michigan State University and B.Sc. in Electronic and Telecommunications Engineering with First Class Honors from University of Moratuwa, Sri Lanka. His current research interests include mining knowledge networks for radicalization detection, Internet of Things, machine learning techniques for graphs, and synthetic data generation for machine learning. His research has been funded by DARPA, NSF, DoJ/NIJ, and industry. He served as a Distinguished Lecturer of the IEEE Communications Society (2014-17), and is currently an ACM Distinguished Speaker. He has served extensively as a consultant to companies ranging from startups to Fortune 100.

If you have any questions about this event, please email
alvinc@uillinois.edu

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