Expander Hierarchies for Normalized Cuts on Graphs
- URL: http://arxiv.org/abs/2406.14111v1
- Date: Thu, 20 Jun 2024 08:50:57 GMT
- Title: Expander Hierarchies for Normalized Cuts on Graphs
- Authors: Kathrin Hanauer, Monika Henzinger, Robin Münk, Harald Räcke, Maximilian Vötsch,
- Abstract summary: We introduce first practically efficient algorithm for computing expander decompositions and their hierarchies.
Our experiments on a variety of large graphs show that our expander-based algorithm outperforms state-of-the-art solvers for normalized cut.
- Score: 3.3385430106181184
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Expander decompositions of graphs have significantly advanced the understanding of many classical graph problems and led to numerous fundamental theoretical results. However, their adoption in practice has been hindered due to their inherent intricacies and large hidden factors in their asymptotic running times. Here, we introduce the first practically efficient algorithm for computing expander decompositions and their hierarchies and demonstrate its effectiveness and utility by incorporating it as the core component in a novel solver for the normalized cut graph clustering objective. Our extensive experiments on a variety of large graphs show that our expander-based algorithm outperforms state-of-the-art solvers for normalized cut with respect to solution quality by a large margin on a variety of graph classes such as citation, e-mail, and social networks or web graphs while remaining competitive in running time.
Related papers
- Faster Inference Time for GNNs using coarsening [1.323700980948722]
coarsening-based methods are used to reduce the graph into a smaller one, resulting in faster computation.
No previous research has tackled the cost during the inference.
This paper presents a novel approach to improve the scalability of GNNs through subgraph-based techniques.
arXiv Detail & Related papers (2024-10-19T06:27:24Z) - 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) - One-step Bipartite Graph Cut: A Normalized Formulation and Its
Application to Scalable Subspace Clustering [56.81492360414741]
We show how to enforce a one-step normalized cut for bipartite graphs, especially with linear-time complexity.
In this paper, we first characterize a novel one-step bipartite graph cut criterion with normalized constraints, and theoretically prove its equivalence to a trace problem.
We extend this cut criterion to a scalable subspace clustering approach, where adaptive anchor learning, bipartite graph learning, and one-step normalized bipartite graph partitioning are simultaneously modeled.
arXiv Detail & Related papers (2023-05-12T11:27:20Z) - K-Core Decomposition on Super Large Graphs with Limited Resources [17.71064869466004]
Recent years have seen rapid growth in the scale of the graph, especially in industrial settings.
Applying K-core decomposition on large graphs has attracted more and more attention from academics and the industry.
We propose a divide-and-conquer strategy on top of the distributed K-core decomposition algorithm.
arXiv Detail & Related papers (2021-12-26T04:34:11Z) - Graph-wise Common Latent Factor Extraction for Unsupervised Graph
Representation Learning [40.70562886682939]
We propose a new principle for unsupervised graph representation learning: Graph-wise Common latent Factor EXtraction (GCFX)
GCFX explicitly extract common latent factors from an input graph and achieve improved results on downstream tasks to the current state-of-the-art.
Through extensive experiments and analysis, we demonstrate that GCFX is beneficial for graph-level tasks to alleviate distractions caused by local variations of individual nodes or local neighbourhoods.
arXiv Detail & Related papers (2021-12-16T12:22:49Z) - Effective and Efficient Graph Learning for Multi-view Clustering [173.8313827799077]
We propose an effective and efficient graph learning model for multi-view clustering.
Our method exploits the view-similar between graphs of different views by the minimization of tensor Schatten p-norm.
Our proposed algorithm is time-economical and obtains the stable results and scales well with the data size.
arXiv Detail & Related papers (2021-08-15T13:14:28Z) - Multilayer Graph Clustering with Optimized Node Embedding [70.1053472751897]
multilayer graph clustering aims at dividing the graph nodes into categories or communities.
We propose a clustering-friendly embedding of the layers of a given multilayer graph.
Experiments show that our method leads to a significant improvement.
arXiv Detail & Related papers (2021-03-30T17:36:40Z) - Graph Coarsening with Neural Networks [8.407217618651536]
We propose a framework for measuring the quality of coarsening algorithm and show that depending on the goal, we need to carefully choose the Laplace operator on the coarse graph.
Motivated by the observation that the current choice of edge weight for the coarse graph may be sub-optimal, we parametrize the weight assignment map with graph neural networks and train it to improve the coarsening quality in an unsupervised way.
arXiv Detail & Related papers (2021-02-02T06:50:07Z) - Multilayer Clustered Graph Learning [66.94201299553336]
We use contrastive loss as a data fidelity term, in order to properly aggregate the observed layers into a representative graph.
Experiments show that our method leads to a clustered clusters w.r.t.
We learn a clustering algorithm for solving clustering problems.
arXiv Detail & Related papers (2020-10-29T09:58:02Z) - Towards Deeper Graph Neural Networks [63.46470695525957]
Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations.
Several recent studies attribute this performance deterioration to the over-smoothing issue.
We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
arXiv Detail & Related papers (2020-07-18T01:11:14Z)
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.