The Dynamics of Multi-Modal Networks

The widespread study of networks in diverse domains, including social, technological, and scientific settings, has increased the interest in statistical and machine learning techniques for network analysis. Many of these networks are complex, involving more than one kind of entity, and multiple relationship types, both changing over time. While there have been many network analysis methods proposed for problems such as network evolution, community detection, information diffusion and opinion leader identification, the majority of these methods assume a single entity type, a single edge type and often no temporal dynamics. One of the main shortcomings of these traditional techniques is their inadequacy for capturing higher-order dependencies often present in real, complex networks. To address these shortcomings, I focus on analysis and inference in dynamic, multi-modal, multi-relational networks, containing multiple entity types (such as people, social groups, organizations, locations, etc.), and different relationship types (such as friendship, membership, affiliation, etc.). An example from social network theory is a network describing users, organizations and interest groups, where users have different types of ties among each other, such as friendship, family ties, etc., as well as affiliation and membership links with organizations and interest groups. By considering the complex structure of these networks rather than limiting the analysis to a single entity or relationship type, I show how we can build richer predictive models that provide better understanding of the network dynamics, and thus result in better quality predictions. In the first part of my dissertation, I address the problems of network evolution and clustering. For network evolution, I describe methods for modeling the interactions between different modalities, and propose a co-evolution model for social and affiliation networks. I then move to the problem of network clustering, where I propose a novel algorithm for clustering multi-modal, multi-relational data. The second part of my dissertation focuses on the temporal dynamics of interactions in complex networks, from both user-level and network-level perspectives. For the user-centric approach, I analyze the dynamics of user relationships with other entity types, proposing a measure of the "loyalty" a user shows for a given group or topic, based on her temporal interaction pattern. I then move to macroscopic-level approaches for analyzing the dynamic processes that occur on a network scale. I propose a new differential adaptive diffusion model for incorporating diversity and trust in the process of information diffusion on multi-modal, multi-relational networks. I also discuss the implications of the proposed diffusion model on designing new strategies for viral marketing and influential detection. I validate all the proposed methods on several real-world networks from multiple domains

RESEARCH STATEMENT

My research interests span the areas of data mining, machine learning, and social network analysis. A common thread in my research is understanding the different types of interactions that occur within social networks, and their effects on the users ’ behavior. In particular, my research focuses on reasoning in dynamic, multi-modal, multi-relational network settings, where there exist multiple entity types (such as people, social groups, organizations, locations, etc.), and different types of relationships (such as friendship, membership, affiliation, etc.). For example, in social network settings, in addition to friendship relationships, users can have family ties with other users, join multiple social groups, be affiliated with various organizations, or belong to different geographical sub-networks. By considering the complex structure of these networks rather than limiting the analysis to a single entity or relationship type, we can build richer models that provide better understanding of the dynamics in these settings, and thus help in providing highly accurate predictions about future events. My long-term research goals are to analyze a number of aspects related to information networks (such as group formation, network clustering, information diffusion, etc.), create explanatory and predictive models of the interactions that occur in complex network settings, and investigate the implications of these studies on applications in different domains. Based on my doctoral research, I believe that capturing different types of dependencies that exist in networked systems is a propitious approach for understanding the dynamics of the corresponding domains, particularly huma

Co-evolution of social and affiliation networks

In our work, we address the problem of modeling social network generation which explains both link and group formation. Recent studies on social network evolution propose generative models which capture the statistical properties of real-world networks related only to node-to-node link formation. We propose a novel model which captures the coevolution of social and affiliation networks. We provide surprising insights into group formation based on observations in several real-world networks, showing that users often join groups for reasons other than their friends. Our experiments show that the model is able to capture both the newly observed and previously studied network properties. This work is the first to propose a generative model which captures the statistical properties of these complex networks. The proposed model facilitates controlled experiments which study the effect of actors ’ behavior on the network evolution, and it allows the generation of realistic synthetic datasets