no code implementations • 7 Jan 2024 • Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan
While graph neural networks (GNNs) are widely used for node and graph representation learning tasks, the reliability of GNN uncertainty estimates under distribution shifts remains relatively under-explored.
no code implementations • 20 Sep 2023 • Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan
Safe deployment of graph neural networks (GNNs) under distribution shift requires models to provide accurate confidence indicators (CI).
no code implementations • 8 Jun 2023 • Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra
We ground the practical implications of this work through granular analysis on five real-world datasets with varying global homophily levels, demonstrating that (a) GNNs can fail to generalize to test nodes that deviate from the global homophily of a graph, and (b) high local homophily does not necessarily confer high performance for a node.
1 code implementation • 23 Aug 2022 • Jing Zhu, Danai Koutra, Mark Heimann
Network alignment, or the task of finding corresponding nodes in different networks, is an important problem formulation in many application domains.
1 code implementation • 4 Aug 2022 • Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan
Overall, our work rigorously contextualizes, both empirically and theoretically, the effects of data-centric properties on augmentation strategies and learning paradigms for graph SSL.
no code implementations • 25 Jul 2022 • Rakshith Subramanyam, Mark Heimann, Jayram Thathachar, Rushil Anirudh, Jayaraman J. Thiagarajan
Model agnostic meta-learning algorithms aim to infer priors from several observed tasks that can then be used to adapt to a new task with few examples.
no code implementations • 10 Jul 2022 • Donald Loveland, Jiong Zhu, Mark Heimann, Ben Fish, Michael T. Schaub, Danai Koutra
We study the task of node classification for graph neural networks (GNNs) and establish a connection between group fairness, as measured by statistical parity and equal opportunity, and local assortativity, i. e., the tendency of linked nodes to have similar attributes.
no code implementations • 9 Jul 2022 • Konstantia Georgouli, Helgi I Ingólfsson, Fikret Aydin, Mark Heimann, Felice C Lightstone, Peer-Timo Bremer, Harsh Bhatia
Capturing intricate biological phenomena often requires multiscale modeling where coarse and inexpensive models are developed using limited components of expensive and high-fidelity models.
no code implementations • 29 Sep 2021 • Puja Trivedi, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan
Using the recent population augmentation graph-based analysis of self-supervised learning, we show theoretically that the success of GCL with popular augmentations is bounded by the graph edit distance between different classes.
1 code implementation • 26 Feb 2021 • Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra
While most network embedding techniques model the relative positions of nodes in a network, recently there has been significant interest in structural embeddings that model node role equivalences, irrespective of their distances to any specific nodes.
1 code implementation • 14 Jan 2021 • Junchen Jin, Mark Heimann, Di Jin, Danai Koutra
While most network embedding techniques model the proximity between nodes in a network, recently there has been significant interest in structural embeddings that are based on node equivalences, a notion rooted in sociology: equivalences or positions are collections of nodes that have similar roles--i. e., similar functions, ties or interactions with nodes in other positions--irrespective of their distance or reachability in the network.
Network Embedding
Social and Information Networks
1 code implementation • 30 Jul 2020 • Kyle K. Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann, Danai Koutra
In this paper, we propose a framework, called G-CREWE (Graph CompREssion With Embedding) to solve the network alignment problem.
4 code implementations • NeurIPS 2020 • Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra
We investigate the representation power of graph neural networks in the semi-supervised node classification task under heterophily or low homophily, i. e., in networks where connected nodes may have different class labels and dissimilar features.
Graph Neural Network
Node Classification on Non-Homophilic (Heterophilic) Graphs
1 code implementation • 10 May 2020 • Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, Danai Koutra
Network alignment, the process of finding correspondences between nodes in different graphs, has many scientific and industrial applications.
1 code implementation • 18 Apr 2019 • Di Jin, Mark Heimann, Ryan Rossi, Danai Koutra
Identity stitching, the task of identifying and matching various online references (e. g., sessions over different devices and timespans) to the same user in real-world web services, is crucial for personalization and recommendations.
1 code implementation • 17 Feb 2018 • Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra
Problems involving multiple networks are prevalent in many scientific and other domains.
Social and Information Networks