48 citations · 213 across the 21 of their papers we have counts for
22 papers
Higher-Order Positional Encodings for Graph Representation Learning
Caleb Stam, Aagrim Hoysal, Sanjukta Krishnagopal
Many real-world systems exhibit higher-order interactions among groups of entities that cannot be captured by pairwise relationships alone. Graph Transformers and Graph Neural Netw…
Sparse Contextual Coupling Reshapes Diffusion Geometry in Multilayer Hypergraphs
Hao Ding, Sanjukta Krishnagopal
Many complex systems combine dense background structure with sparse contextual information. We introduce a diffusion-based framework for analyzing how sparse condition-specific lay…
Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks
Sanjukta Krishnagopal
Spectral graph sparsification is a classical tool for reducing graph complexity while preserving Laplacian quadratic forms. In graph neural networks (GNNs), sparsification is often…
Topological Neural Tangent Kernel
Sanjukta Krishnagopal
Graph neural tangent kernels give a principled infinite-width theory for graph neural networks, but inherit a basic limitation of graph models: they see only pairwise structure. Ma…
Fractal dimensions of complex networks: advocating for a topological approach
Rayna Andreeva, Haydeé Contreras-Peruyero, Sanjukta Krishnagopal +3
Topological Data Analysis (TDA) uses insights from topology to create representations of data able to capture global and local geometric and topological properties. Its methods hav…
Beyond Attention: Learning Spatio-Temporal Dynamics with Emergent Interpretable Topologies
Sai Vamsi Alisetti, Vikas Kalagi, Sanjukta Krishnagopal
Spatio-temporal forecasting is critical in applications such as traffic prediction, energy demand modeling, and weather monitoring. While Graph Attention Networks (GATs) are popula…
A Network-Based Measure of Cosponsorship Influence on Bill Passing in the United States House of Representatives
Sarah Sotoudeh, Mason A. Porter, Sanjukta Krishnagopal
Each year, the United States Congress considers thousands of legislative proposals to select bills to present to the US President to sign into law. Naturally, the decision processe…
Bounded-Confidence Models of Opinion Dynamics with Neighborhood Effects
Sanjukta Krishnagopal, Mason A. Porter
We generalize bounded-confidence models (BCMs) of opinion dynamics by incorporating neighborhood effects. In a BCM, interacting agents influence each other through dyadic influence…
Hate speech and hate crimes: a data-driven study of evolving discourse around marginalized groups
Malvina Bozhidarova, Jonathn Chang, Aaishah Ale-rasool +6
This study explores the dynamic relationship between online discourse, as observed in tweets, and physical hate crimes, focusing on marginalized groups. Leveraging natural language…
Topology and dynamics of higher-order multiplex networks
Sanjukta Krishnagopal, Ginestra Bianconi
Higher-order networks are gaining significant scientific attention due to their ability to encode the many-body interactions present in complex systems. However, higher-order netwo…
Graph Neural Tangent Kernel: Convergence on Large Graphs
Sanjukta Krishnagopal, Luana Ruiz
Graph neural networks (GNNs) achieve remarkable performance in graph machine learning tasks but can be hard to train on large-graph data, where their learning dynamics are not well…
Local Dirac Synchronization on Networks
Lucille Calmon, Sanjukta Krishnagopal, Ginestra Bianconi
We propose Local Dirac Synchronization which uses the Dirac operator to capture the dynamics of coupled nodes and link signals on an arbitrary network. In Local Dirac Synchronizati…
The collective vs individual nature of mountaineering: a network and simplicial approach
Sanjukta Krishnagopal
Mountaineering is a sport of contrary forces: teamwork plays a large role in mental fortitude and skills, but the actual act of climbing, and indeed survival, is largely individual…
Encoding priors in the brain: a reinforcement learning model for mouse decision making
Sanjukta Krishnagopal, Peter Latham
In two-alternative forced choice tasks, prior knowledge can improve performance, especially when operating near the psychophysical threshold. For instance, if subjects know that on…
Stroke recovery phenotyping through network trajectory approaches and graph neural networks
Sanjukta Krishnagopal, Keith Lohse, Robynne Braun
Stroke is a leading cause of neurological injury characterized by impairments in multiple neurological domains including cognition, language, sensory and motor functions. Clinical…
Success at high peaks: a multiscale approach combining individual and expedition-wide factors
Sanjukta Krishnagopal
This work presents a network-based data-driven study of the combination of factors that contribute to success in mountaineering. It simultaneously examines the effects of individua…
Spectral Detection of Simplicial Communities via Hodge Laplacians
Sanjukta Krishnagopal, Ginestra Bianconi
Despite being a source of rich information, graphs are limited to pairwise interactions. However, several real-world networks such as social networks, neuronal networks, etc., invo…
Encoded Prior Sliced Wasserstein AutoEncoder for learning latent manifold representations
Sanjukta Krishnagopal, Jacob Bedrossian
While variational autoencoders have been successful in several tasks, the use of conventional priors are limited in their ability to encode the underlying structure of input data.…
Multi-layer Trajectory Clustering: A Network Algorithm for Disease Subtyping
Sanjukta Krishnagopal
Many diseases display heterogeneity in clinical features and their progression, indicative of the existence of disease subtypes. Extracting patterns of disease variable progression…
Separation of Chaotic Signals by Reservoir Computing
Sanjukta Krishnagopal, Michelle Girvan, Edward Ott +1
We demonstrate the utility of machine learning in the separation of superimposed chaotic signals using a technique called Reservoir Computing. We assume no knowledge of the dynamic…
Identifying and Predicting Parkinson's Disease Subtypes through Trajectory Clustering via Bipartite Networks
Sanjukta Krishnagopal, Rainer Von Coelln, Lisa M. Shulman +1
Parkinson's disease (PD) is a common neurodegenerative disease with a high degree of heterogeneity in its clinical features, rate of progression, and change of variables over time.…
Synchronization Patterns: From Network Motifs to Hierarchical Networks
Sanjukta Krishnagopal, Judith Lehnert, Winnie Poel +2
We investigate complex synchronization patterns such as cluster synchronization and partial amplitude death in networks of coupled Stuart-Landau oscillators with fractal connectivi…