About Graph Degeneracy, Representation Learning and Scalability

• URL: http://arxiv.org/abs/2009.02085v1
• Date: Fri, 4 Sep 2020 09:39:43 GMT
• Title: About Graph Degeneracy, Representation Learning and Scalability
• Authors: Simon Brandeis, Adrian Jarret, Pierre Sevestre
• Abstract summary: We present two techniques taking advantage of the K-Core Decomposition to reduce the time and memory consumption of walk-based Graph Representation Learning algorithms. We evaluate the performances, expressed in terms of quality of embedding and computational resources, of the proposed techniques on several academic datasets.
• Score: 2.029783382155471
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graphs or networks are a very convenient way to represent data with lots of interaction. Recently, Machine Learning on Graph data has gained a lot of traction. In particular, vertex classification and missing edge detection have very interesting applications, ranging from drug discovery to recommender systems. To achieve such tasks, tremendous work has been accomplished to learn embedding of nodes and edges into finite-dimension vector spaces. This task is called Graph Representation Learning. However, Graph Representation Learning techniques often display prohibitive time and memory complexities, preventing their use in real-time with business size graphs. In this paper, we address this issue by leveraging a degeneracy property of Graphs - the K-Core Decomposition. We present two techniques taking advantage of this decomposition to reduce the time and memory consumption of walk-based Graph Representation Learning algorithms. We evaluate the performances, expressed in terms of quality of embedding and computational resources, of the proposed techniques on several academic datasets. Our code is available at https://github.com/SBrandeis/kcore-embedding

Related papers

• Learning on Large Graphs using Intersecting Communities [13.053266613831447]
  MPNNs iteratively update each node's representation in an input graph by aggregating messages from the node's neighbors. MPNNs might quickly become prohibitive for large graphs provided they are not very sparse. We propose approximating the input graph as an intersecting community graph (ICG) -- a combination of intersecting cliques.
  arXiv  Detail & Related papers  (2024-05-31T09:26:26Z)
• Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding [51.75091298017941]
  This paper proposes a novel Deep Manifold (Variational) Graph Auto-Encoder (DMVGAE/DMGAE) for attributed graph data. The proposed method surpasses state-of-the-art baseline algorithms by a significant margin on different downstream tasks across popular datasets.
  arXiv  Detail & Related papers  (2024-01-12T17:57:07Z)
• NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification [70.51126383984555]
  We introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes. The efficient computation is enabled by a kernerlized Gumbel-Softmax operator. Experiments demonstrate the promising efficacy of the method in various tasks including node classification on graphs.
  arXiv  Detail & Related papers  (2023-06-14T09:21:15Z)
• Learnable Graph Matching: A Practical Paradigm for Data Association [74.28753343714858]
  We propose a general learnable graph matching method to address these issues. Our method achieves state-of-the-art performance on several MOT datasets. For image matching, our method outperforms state-of-the-art methods on a popular indoor dataset, ScanNet.
  arXiv  Detail & Related papers  (2023-03-27T17:39:00Z)
• State of the Art and Potentialities of Graph-level Learning [54.68482109186052]
  Graph-level learning has been applied to many tasks including comparison, regression, classification, and more. Traditional approaches to learning a set of graphs rely on hand-crafted features, such as substructures. Deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations.
  arXiv  Detail & Related papers  (2023-01-14T09:15:49Z)
• Scaling R-GCN Training with Graph Summarization [71.06855946732296]
  Training of Relation Graph Convolutional Networks (R-GCN) does not scale well with the size of the graph. In this work, we experiment with the use of graph summarization techniques to compress the graph. We obtain reasonable results on the AIFB, MUTAG and AM datasets.
  arXiv  Detail & Related papers  (2022-03-05T00:28:43Z)
• Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking [58.30147362745852]
  Data association across frames is at the core of Multiple Object Tracking (MOT) task. Existing methods mostly ignore the context information among tracklets and intra-frame detections. We propose a novel learnable graph matching method to address these issues.
  arXiv  Detail & Related papers  (2021-03-30T08:58:45Z)
• Co-embedding of Nodes and Edges with Graph Neural Networks [13.020745622327894]
  Graph embedding is a way to transform and encode the data structure in high dimensional and non-Euclidean feature space. CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space. Our approach achieves or matches the state-of-the-art performance in four graph learning tasks.
  arXiv  Detail & Related papers  (2020-10-25T22:39:31Z)
• Understanding Coarsening for Embedding Large-Scale Graphs [3.6739949215165164]
  Proper analysis of graphs with Machine Learning (ML) algorithms has the potential to yield far-reaching insights into many areas of research and industry. The irregular structure of graph data constitutes an obstacle for running ML tasks on graphs. We analyze the impact of the coarsening quality on the embedding performance both in terms of speed and accuracy.
  arXiv  Detail & Related papers  (2020-09-10T15:06:33Z)
• Graph topology inference benchmarks for machine learning [16.857405938139525]
  We introduce several benchmarks specifically designed to reveal the relative merits and limitations of graph inference methods. We also contrast some of the most prominent techniques in the literature.
  arXiv  Detail & Related papers  (2020-07-16T09:40:32Z)
• Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative Sampling [0.456877715768796]
  We build upon the fact that the skip-gram embedding approach implicitly performs a matrix factorization. We show that higher-order skip-gram with negative sampling is able to disentangle the role of nodes and time. We empirically evaluate our approach using time-resolved face-to-face proximity data, showing that the learned time-varying graph representations outperform state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-06-25T12:04:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.