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Learning over Families of Sets - Hypergraph Representation Learning for Higher Order Tasks
Graph representation learning has made major strides over the past decade. However, in many relational domains, the input data are not …
Balasubramaniam Srinivasan, Da Zheng, George Karypis
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On the Equivalence between Positional Node Embeddings and Structural Graph Representations
This work provides the first unifying theoretical framework for node (positional) embeddings and structural graph representations, …
Balasubramaniam Srinivasan, Bruno Ribeiro
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Relational Pooling for Graph Representations
This work generalizes graph neural networks (GNNs) beyond those based on the Weisfeiler-Lehman (WL) algorithm, graph Laplacians, and …
Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro
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Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs
We consider a simple and overarching representation for permutation-invariant functions of sequences (or multiset functions). Our …
Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro
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NetGloVe: Learning Node Representations for Community Detection

Community detection is a fundamental task in network analysis, with plenty of applications in social networking, biology and …
Balasubramaniam Srinivasan, Jeyavaishnavi Muralikumar*, Kumaran Gunasekaran*, Sudarshan Srinivasa Ramanujam*, Fragkiskos Malliaros
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