TPM: Transition probability matrix - Graph structural feature based embedding

Sarmad N. Mohammed; Semra Gündüç

Kybernetika (2023)

  • Volume: 59, Issue: 2, page 234-253
  • ISSN: 0023-5954

Abstract

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In this work, Transition Probability Matrix (TPM) is proposed as a new method for extracting the features of nodes in the graph. The proposed method uses random walks to capture the connectivity structure of a node's close neighborhood. The information obtained from random walks is converted to anonymous walks to extract the topological features of nodes. In the embedding process of nodes, anonymous walks are used since they capture the topological similarities of connectivities better than random walks. Therefore the obtained embedding vectors have richer information about the underlying connectivity structure. The method is applied to node classification and link prediction tasks. The performance of the proposed algorithm is superior to the state-of-the-art algorithms in the recent literature. Moreover, the extracted information about the connectivity structure of similar networks is used to link prediction and node classification tasks for a completely new graph.

How to cite

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Mohammed, Sarmad N., and Gündüç, Semra. "TPM: Transition probability matrix - Graph structural feature based embedding." Kybernetika 59.2 (2023): 234-253. <http://eudml.org/doc/299089>.

@article{Mohammed2023,
abstract = {In this work, Transition Probability Matrix (TPM) is proposed as a new method for extracting the features of nodes in the graph. The proposed method uses random walks to capture the connectivity structure of a node's close neighborhood. The information obtained from random walks is converted to anonymous walks to extract the topological features of nodes. In the embedding process of nodes, anonymous walks are used since they capture the topological similarities of connectivities better than random walks. Therefore the obtained embedding vectors have richer information about the underlying connectivity structure. The method is applied to node classification and link prediction tasks. The performance of the proposed algorithm is superior to the state-of-the-art algorithms in the recent literature. Moreover, the extracted information about the connectivity structure of similar networks is used to link prediction and node classification tasks for a completely new graph.},
author = {Mohammed, Sarmad N., Gündüç, Semra},
journal = {Kybernetika},
keywords = {graph representation learning; feature learning; link prediction; node classification; anonymous random walk},
language = {eng},
number = {2},
pages = {234-253},
publisher = {Institute of Information Theory and Automation AS CR},
title = {TPM: Transition probability matrix - Graph structural feature based embedding},
url = {http://eudml.org/doc/299089},
volume = {59},
year = {2023},
}

TY - JOUR
AU - Mohammed, Sarmad N.
AU - Gündüç, Semra
TI - TPM: Transition probability matrix - Graph structural feature based embedding
JO - Kybernetika
PY - 2023
PB - Institute of Information Theory and Automation AS CR
VL - 59
IS - 2
SP - 234
EP - 253
AB - In this work, Transition Probability Matrix (TPM) is proposed as a new method for extracting the features of nodes in the graph. The proposed method uses random walks to capture the connectivity structure of a node's close neighborhood. The information obtained from random walks is converted to anonymous walks to extract the topological features of nodes. In the embedding process of nodes, anonymous walks are used since they capture the topological similarities of connectivities better than random walks. Therefore the obtained embedding vectors have richer information about the underlying connectivity structure. The method is applied to node classification and link prediction tasks. The performance of the proposed algorithm is superior to the state-of-the-art algorithms in the recent literature. Moreover, the extracted information about the connectivity structure of similar networks is used to link prediction and node classification tasks for a completely new graph.
LA - eng
KW - graph representation learning; feature learning; link prediction; node classification; anonymous random walk
UR - http://eudml.org/doc/299089
ER -

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