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Found 16 papers, 9 papers with code
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks
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.
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks
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
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.
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment
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
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 Knowledge-Augmented Meta-Learning for Few-Shot Classification
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.
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods
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.
Emerging Patterns in the Continuum Representation of Protein-Lipid Fingerprints
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
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.
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding
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.
Towards Understanding and Evaluating Structural Node Embeddings
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
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
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.
CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding
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
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
Problems involving multiple networks are prevalent in many scientific and other domains.
Social and Information Networks
