Search Results for author: Mark Heimann

Found 15 papers, 8 papers with code

Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks

no code implementations20 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).

On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks

no code implementations8 Jun 2023 Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Shaub, 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.

Node Classification

CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment

1 code implementation23 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.

Analyzing Data-Centric Properties for Graph Contrastive Learning

1 code implementation4 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.

Contrastive Learning Self-Supervised Learning +1

Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification

no code implementations25 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.

Classification Few-Shot Learning

On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods

no code implementations10 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.

Fairness Node Classification

Emerging Patterns in the Continuum Representation of Protein-Lipid Fingerprints

no code implementations9 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.


Interrogating Paradigms in Self-supervised Graph Representation Learning

no code implementations29 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.

Contrastive Learning Graph Representation Learning +2

Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding

1 code implementation26 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.

Graph Embedding Network Embedding

Towards Understanding and Evaluating Structural Node Embeddings

1 code implementation14 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

G-CREWE: Graph CompREssion With Embedding for Network Alignment

1 code implementation30 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.

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs

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.

Node Classification on Non-Homophilic (Heterophilic) Graphs

CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding

no code implementations10 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.

node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching

1 code implementation18 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.


REGAL: Representation Learning-based Graph Alignment

1 code implementation17 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

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