Representation learning for homophilic preferences

National Research Foundation (NRF) Singapor

Challenging Low Homophily in Social Recommendation

Social relations are leveraged to tackle the sparsity issue of user-item interaction data in recommendation under the assumption of social homophily. However, social recommendation paradigms predominantly focus on homophily based on user preferences. While social information can enhance recommendations, its alignment with user preferences is not guaranteed, thereby posing the risk of introducing informational redundancy. We empirically discover that social graphs in real recommendation data exhibit low preference-aware homophily, which limits the effect of social recommendation models. To comprehensively extract preference-aware homophily information latent in the social graph, we propose Social Heterophily-alleviating Rewiring (SHaRe), a data-centric framework for enhancing existing graph-based social recommendation models. We adopt Graph Rewiring technique to capture and add highly homophilic social relations, and cut low homophilic (or heterophilic) relations. To better refine the user representations from reliable social relations, we integrate a contrastive learning method into the training of SHaRe, aiming to calibrate the user representations for enhancing the result of Graph Rewiring. Experiments on real-world datasets show that the proposed framework not only exhibits enhanced performances across varying homophily ratios but also improves the performance of existing state-of-the-art (SOTA) social recommendation models.Comment: This paper has been accepted by The Web Conference (WWW) 202

Growing Attributed Networks through Local Processes

This paper proposes an attributed network growth model. Despite the knowledge that individuals use limited resources to form connections to similar others, we lack an understanding of how local and resource-constrained mechanisms explain the emergence of rich structural properties found in real-world networks. We make three contributions. First, we propose a parsimonious and accurate model of attributed network growth that jointly explains the emergence of in-degree distributions, local clustering, clustering-degree relationship and attribute mixing patterns. Second, our model is based on biased random walks and uses local processes to form edges without recourse to global network information. Third, we account for multiple sociological phenomena: bounded rationality, structural constraints, triadic closure, attribute homophily, and preferential attachment. Our experiments indicate that the proposed Attributed Random Walk (ARW) model accurately preserves network structure and attribute mixing patterns of six real-world networks; it improves upon the performance of eight state-of-the-art models by a statistically significant margin of 2.5-10x.Comment: 11 pages, 13 figure

Diversity and Popularity in Social Networks

Homophily, the tendency of linked agents to have similar characteristics, is an important feature of social networks. We present a new model of network formation that allows the linking process to depend on individuals types and study the impact of such a bias on the network structure. Our main results fall into three categories: (i) we compare the distributions of intra- and inter-group links in terms of stochastic dominance, (ii) we show how, at the group level, homophily depends on the groups size and the details of the formation process, and (iii) we understand precisely the determinants of local homophily at the individual level. Especially, we find that popular individuals have more diverse networks. Our results are supported empirically in the AddHealth data looking at networks of social connections between boys and girls.Social networks, Network formation, Homophily, Diversity

Network Structure, Interracial Contacts, and the Evolution of Social Norms

In this paper I explore the underlying mechanisms of the changes in public discourse with respect to the issue of racial equality that have been observed in the United States over the course of its history, with a particular focus on the changes that occurred in the latter half of the twentieth century. Specifically, I provide a formal model of social interactions in which agents are assigned to non-homophilic networks, are heterogeneous with respect to preferences for equality between the races, and have preferences both to express their true preferences and to not appear deviant from the group. In a series of numerical experiments, results indicate that the probability of a transition in norms from an equilibrium around inequality to an equilibrium around equality is increasing in the size of the minority population and decreasing in the size of groups to which individuals are assigned

Bounded Confidence: How AI Could Exacerbate Social Media’s Homophily Problem

The advent of the Internet was heralded as a revolutionary development in the democratization of information. It has emerged, however, that online discourse on social media tends to narrow the information landscape of its users. This dynamic is driven by the propensity of the network structure of social media to tend toward homophily; users strongly prefer to interact with content and other users that are similar to them. We review the considerable evidence for the ubiquity of homophily in social media, discuss some possible mechanisms for this phenomenon, and present some observed and hypothesized effects. We also discuss how the homophilic structure of social media makes it uniquely vulnerable to artificial-intelligence-driven, automated influence campaigns

Expanding Social Network Modeling Software and Agent Models for Diffusion Processes

In an increasingly digitally interconnected world, the study of social networks and their dynamics is burgeoning. Anthropologically, the ubiquity of online social networks has had striking implications for the condition of large portions of humanity. This technology has facilitated content creation of virtually all sorts, information sharing on an unprecedented scale, and connections and communities among people with similar interests and skills. The first part of my research is a social network evolution and visualization engine. Built on top of existing technologies, my software is designed to provide abstractions from the underlying libraries, drive real-time network evolution based on user-defined parameters, and optionally visualize that evolution at each step of the process. My software provides a low maintenance interface for the creation of networks and update schemes for a wide array of experimental contexts, an engine to drive network evolution, and a visualization platform to provide real-time feedback about different aspects of the network to the researcher, as well as fine-grained debugging tools. We conducted investigations into the opinion dynamics of networks when multiple agent “archetypes” interact together with this platform. We modeled agents’ archetypes with respect to two attributes: their preference over their friends’ opinion profiles, and their tendency to change their opinion over time. We extended the current state of agent modeling in opinion diffusion by providing a unified 2D trajectory/preference space for agents that incorporates most common models in the literature. We investigated six agent archetypes from this space, and examined the behavior of the network as a whole and the individual agents in a variety of contexts. In another branch of work using our software, we developed a network of agents who must carry out both economic and social activities during a pandemic. Agents’ decisions about what actions to take (self-protective measures like masking, social distancing, or waiting to run errands) are based on several factors, including perception of risk (obtained from news reports, social connections, etc.) and economic need. We show with preliminary testing that this platform is able to execute standard pandemic models successfully with the incorporation of the economic and social dimensions, and that this paradigm may provide useful insight into effective agent-level response policies that can be used in concert with other top-down approaches that comprise most of the recent pandemic response research. We have investigated the implications of varying behavior profiles within a network of agents, and how those behavioral compositions affect the overall climate of the network in return, and this software will continue to facilitate similar research into the future

Deformable Graph Transformer

Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision. However, the success is limited to small-scale graphs due to the drawbacks of full dot-product attention on graphs such as the quadratic complexity with respect to the number of nodes and message aggregation from enormous irrelevant nodes. To address these issues, we propose Deformable Graph Transformer (DGT) that performs sparse attention via dynamically sampled relevant nodes for efficiently handling large-scale graphs with a linear complexity in the number of nodes. Specifically, our framework first constructs multiple node sequences with various criteria to consider both structural and semantic proximity. Then, combining with our learnable Katz Positional Encodings, the sparse attention is applied to the node sequences for learning node representations with a significantly reduced computational cost. Extensive experiments demonstrate that our DGT achieves state-of-the-art performance on 7 graph benchmark datasets with 2.5 - 449 times less computational cost compared to transformer-based graph models with full attention.Comment: 16 pages, 3 figure