Arman Gupta

Tony Gracious, Arman Gupta, Ambedkar Dukkipati

Forecasting relations between entities is paramount in the current era of data and AI. However, it is often overlooked that real-world relationships are inherently directional, involve more than two entities, and can change with time. In this paper, we provide a comprehensive solution to the problem of forecasting directional relations in a general setting, where relations are higher-order, i.e., directed hyperedges in a hypergraph. This problem has not been previously explored in the existing literature. The primary challenge in solving this problem is that the number of possible hyperedges is exponential in the number of nodes at each event time. To overcome this, we propose a sequential generative approach that segments the forecasting process into multiple stages, each contingent upon the preceding stages, thereby reducing the search space involved in predictions of hyperedges. The first stage involves a temporal point process-based node event forecasting module that identifies the subset of nodes involved in an event. The second stage is a candidate generation module that predicts hyperedge sizes and adjacency vectors for nodes observing events. The final stage is a directed hyperedge predictor that identifies the truth by searching over the set of candidate hyperedges. To validate the effectiveness of our model, we compiled five datasets and conducted an extensive empirical study to assess each downstream task. Our proposed method achieves a performance gain of 32\% and 41\% compared to the state-of-the-art pairwise and hyperedge event forecasting models, respectively, for the event type prediction.

Arman Gupta, Govind Waghmare, Gaurav Oberoi et al.

In heterophilic graphs, where neighboring nodes often belong to different classes, conventional Graph Neural Networks (GNNs) struggle due to their reliance on local homophilous neighborhoods. Prior studies suggest that modeling edge directionality in such graphs can increase effective homophily and improve classification performance. Simultaneously, recent work on polynomially expressive GNNs shows promise in capturing higher-order interactions among features. In this work, we study the combined effect of edge directionality and expressive message passing on node classification in heterophilic graphs. Specifically, we propose two architectures: (1) a polynomially expressive GAT baseline (Poly), and (2) a direction-aware variant (Dir-Poly) that separately aggregates incoming and outgoing edges. Both models are designed to learn permutation-equivariant high-degree polynomials over input features, while remaining scalable with no added time complexity. Experiments on five benchmark heterophilic datasets show that our Poly model consistently outperforms existing baselines, and that Dir-Poly offers additional gains on graphs with inherent directionality (e.g., Roman Empire), achieving state-of-the-art results. Interestingly, on undirected graphs, introducing artificial directionality does not always help, suggesting that the benefit of directional message passing is context-dependent. Our findings highlight the complementary roles of edge direction and expressive feature modeling in heterophilic graph learning.