Inductive Graph Embeddings through Locality Encodings

• URL: http://arxiv.org/abs/2009.12585v1
• Date: Sat, 26 Sep 2020 13:09:11 GMT
• Title: Inductive Graph Embeddings through Locality Encodings
• Authors: Nurudin Alvarez-Gonzalez, Andreas Kaltenbrunner, Vicen\c{c} G\'omez
• Abstract summary: We look at the problem of finding inductive network embeddings in large networks without domain-dependent node/edge attributes. We propose to use a set of basic predefined local encodings as the basis of a learning algorithm. This method achieves state-of-the-art performance in tasks such as role detection, link prediction and node classification.
• Score: 0.42970700836450487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning embeddings from large-scale networks is an open challenge. Despite the overwhelming number of existing methods, is is unclear how to exploit network structure in a way that generalizes easily to unseen nodes, edges or graphs. In this work, we look at the problem of finding inductive network embeddings in large networks without domain-dependent node/edge attributes. We propose to use a set of basic predefined local encodings as the basis of a learning algorithm. In particular, we consider the degree frequencies at different distances from a node, which can be computed efficiently for relatively short distances and a large number of nodes. Interestingly, the resulting embeddings generalize well across unseen or distant regions in the network, both in unsupervised settings, when combined with language model learning, as well as in supervised tasks, when used as additional features in a neural network. Despite its simplicity, this method achieves state-of-the-art performance in tasks such as role detection, link prediction and node classification, and represents an inductive network embedding method directly applicable to large unattributed networks.

Related papers

• Collaborative Graph Neural Networks for Attributed Network Embedding [63.39495932900291]
  Graph neural networks (GNNs) have shown prominent performance on attributed network embedding. We propose COllaborative graph Neural Networks--CONN, a tailored GNN architecture for network embedding.
  arXiv  Detail & Related papers  (2023-07-22T04:52:27Z)
• Community detection using low-dimensional network embedding algorithms [1.052782170493037]
  We rigorously understand the performance of two major algorithms, DeepWalk and node2vec, in recovering communities for canonical network models. We prove that, given some fixed co-occurrence window, node2vec using random walks with a low non-backtracking probability can succeed for much sparser networks.
  arXiv  Detail & Related papers  (2021-11-04T14:57:43Z)
• Unsupervised Domain-adaptive Hash for Networks [81.49184987430333]
  Domain-adaptive hash learning has enjoyed considerable success in the computer vision community. We develop an unsupervised domain-adaptive hash learning method for networks, dubbed UDAH.
  arXiv  Detail & Related papers  (2021-08-20T12:09:38Z)
• Temporal Graph Network Embedding with Causal Anonymous Walks Representations [54.05212871508062]
  We propose a novel approach for dynamic network representation learning based on Temporal Graph Network. For evaluation, we provide a benchmark pipeline for the evaluation of temporal network embeddings. We show the applicability and superior performance of our model in the real-world downstream graph machine learning task provided by one of the top European banks.
  arXiv  Detail & Related papers  (2021-08-19T15:39:52Z)
• Learning for Integer-Constrained Optimization through Neural Networks with Limited Training [28.588195947764188]
  We introduce a symmetric and decomposed neural network structure, which is fully interpretable in terms of the functionality of its constituent components. By taking advantage of the underlying pattern of the integer constraint, the introduced neural network offers superior generalization performance with limited training. We show that the introduced decomposed approach can be further extended to semi-decomposed frameworks.
  arXiv  Detail & Related papers  (2020-11-10T21:17:07Z)
• Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks [78.65792427542672]
  Dynamic Graph Network (DG-Net) is a complete directed acyclic graph, where the nodes represent convolutional blocks and the edges represent connection paths. Instead of using the same path of the network, DG-Net aggregates features dynamically in each node, which allows the network to have more representation ability.
  arXiv  Detail & Related papers  (2020-10-02T16:50:26Z)
• DINE: A Framework for Deep Incomplete Network Embedding [33.97952453310253]
  We propose a Deep Incomplete Network Embedding method, namely DINE. We first complete the missing part including both nodes and edges in a partially observable network by using the expectation-maximization framework. We evaluate DINE over three networks on multi-label classification and link prediction tasks.
  arXiv  Detail & Related papers  (2020-08-09T04:59:35Z)
• Graph Prototypical Networks for Few-shot Learning on Attributed Networks [72.31180045017835]
  We propose a graph meta-learning framework -- Graph Prototypical Networks (GPN) GPN is able to perform textitmeta-learning on an attributed network and derive a highly generalizable model for handling the target classification task.
  arXiv  Detail & Related papers  (2020-06-23T04:13:23Z)
• Progressive Graph Convolutional Networks for Semi-Supervised Node Classification [97.14064057840089]
  Graph convolutional networks have been successful in addressing graph-based tasks such as semi-supervised node classification. We propose a method to automatically build compact and task-specific graph convolutional networks.
  arXiv  Detail & Related papers  (2020-03-27T08:32:16Z)

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.