Temporal Graph Networks combine memory modules and graph operators to learn on dynamic graphs as timed event sequences, outperforming prior methods on transductive and inductive tasks while unifying earlier models as special cases.
Representation Learning on Graphs: Methods and Applications
6 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph (e.g., degree statistics or kernel functions). However, recent years have seen a surge in approaches that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. Here we provide a conceptual review of key advancements in this area of representation learning on graphs, including matrix factorization-based methods, random-walk based algorithms, and graph neural networks. We review methods to embed individual nodes as well as approaches to embed entire (sub)graphs. In doing so, we develop a unified framework to describe these recent approaches, and we highlight a number of important applications and directions for future work.
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Presents YoutubeGraph-Dyn, a multi-modal dynamic graph dataset from YouTube interactions with intra-day snapshots, and benchmarks clustering for community migration plus time series and RNN methods for forecasting non-timestamped attributes.
VLMs possess a latent 3D scene topology subspace corresponding to Laplacian eigenmaps that can be causally shaped via Dirichlet energy regularization to improve spatial task performance by up to 12.1%.
Hierarchical multigraph GCNs applied to superpixels achieve competitive or superior accuracy to CNNs on standard image classification benchmarks.
NECL uses neighborhood-similarity graph compression as a preprocessing step to accelerate random-walk network embedding algorithms without reducing their effectiveness on classification tasks.
DCR augments seq2seq with latent topics for topic control, GCN for venue matching, and pointer networks for response generation, reporting superior performance over baselines on a multi-turn travel dialog dataset.
citing papers explorer
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Temporal Graph Networks for Deep Learning on Dynamic Graphs
Temporal Graph Networks combine memory modules and graph operators to learn on dynamic graphs as timed event sequences, outperforming prior methods on transductive and inductive tasks while unifying earlier models as special cases.
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Tracking Temporal Evolution of Graphs using Non-Timestamped Data
Presents YoutubeGraph-Dyn, a multi-modal dynamic graph dataset from YouTube interactions with intra-day snapshots, and benchmarks clustering for community migration plus time series and RNN methods for forecasting non-timestamped attributes.
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Uncovering and Shaping the Latent Representation of 3D Scene Topology in Vision-Language Models
VLMs possess a latent 3D scene topology subspace corresponding to Laplacian eigenmaps that can be causally shaped via Dirichlet energy regularization to improve spatial task performance by up to 12.1%.
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Image Classification with Hierarchical Multigraph Networks
Hierarchical multigraph GCNs applied to superpixels achieve competitive or superior accuracy to CNNs on standard image classification benchmarks.
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Network Embedding: on Compression and Learning
NECL uses neighborhood-similarity graph compression as a preprocessing step to accelerate random-walk network embedding algorithms without reducing their effectiveness on classification tasks.
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Deep Conversational Recommender in Travel
DCR augments seq2seq with latent topics for topic control, GCN for venue matching, and pointer networks for response generation, reporting superior performance over baselines on a multi-turn travel dialog dataset.