Diffusion models have excelled in generating natural images and are now being adapted to a variety of data types, including graphs. However, conventional models often rely on Gaussian or categorical diffusion processes, which can struggle to accommodate the mixed discrete and continuous components characteristic of graph data. Graphs typically feature discrete structures and continuous node attributes that often exhibit rich statistical patterns, including sparsity, bounded ranges, skewed distributions, and long-tailed behavior. To address these challenges, we introduce Graph Beta Diffusion (GBD), a generative model specifically designed to handle the diverse nature of graph data. GBD leverages a beta diffusion process, effectively modeling both continuous and discrete elements. Additionally, we propose a modulation technique that enhances the realism of generated graphs by stabilizing critical graph topology while maintaining flexibility for other components. GBD competes strongly with existing models across multiple general and biochemical graph benchmarks, showcasing its ability to capture the intricate balance between discrete and continuous features inherent in real-world graph data. Our PyTorch code is available at https://github.com/xinyangATK/GraphBetaDiffusion.

Graph neural networks (GNNs), which propagate the node features through the edges and learn how to transform the aggregated features under label supervision, have achieved great success in supervised feature extraction for both node-level and graph-level classification tasks. However, GNNs typically treat the graph structure as given and ignore how the edges are formed. This paper introduces a graph generative process to model how the observed edges are generated by aggregating the node interactions over a set of overlapping node communities, each of which contributes to the edges via a logical OR mechanism. Based on this generative model, we partition each edge into the summation of multiple community-specific weighted edges and use them to define community-specific GNNs. A variational inference framework is proposed to jointly learn a GNN-based inference network that partitions the edges into different communities, these community-specific GNNs, and a GNN-based predictor that combines community-specific GNNs for the end classification task. Extensive evaluations on real-world graph datasets have verified the effectiveness of the proposed method in learning discriminative representations for both node-level and graph-level classification tasks.

In this work, we propose a major-subordinate-task learning framework to estimate image orientation. The involved two tasks, regression to the characteristic orientation of the image (major) and classification by visual content (subordinate), are fed with shared feature and update feature extractor collaboratively. To boost the major task, we introduce a novel module, matched gradients weight multiplier, to calculate matching degree of the two tasks and adaptively adjust feedback from the subordinate task towards the shared feature extractor accordingly. As a result, such feedback is expected to be always promotive to the major task. Experiments demonstrate the effectiveness of our proposed framework over the counterpart settings.

Deviant behavior in online social systems is a difficult problem to address. Consequences of deviance include driving off users and tarnishing the system's public image. We present an examination of these concepts in a popular online game, League of Legends. Using a large collection of game records and player-given feedback, we develop a metric, toxicity index, to identify deviant players. We then look at the effects of interacting with deviant players, including effects on retention. We find that toxic players have several significant predictive patterns, such as playing in more competitive game modes and playing with friends. We also show that toxic players drive away new players, but that experienced players are more resilient to deviant behavior. Based on our findings, we suggest methods to better identify and counteract the negative effects of deviance.