Analysis of Annotated Social and Information Networks

Savić, Miloš

doi:10.1145/3227609.3227693

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…For many networks labels indicating categories or classes could be assigned to nodes [28,33]. For example, in article citation networks node labels indicate research fields, in scientific co-authorship networks node labels correspond to the institutional affiliation of researchers, in the WWW network node labels could denote topics of WWW pages, and so on.…”

Section: Introductionmentioning

confidence: 99%

Hub‐aware random walk graph embedding methods for classification

Tomčić,

Savić,

Radovanović

2024

Statistical Analysis

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In the last two decades, we are witnessing a huge increase of valuable big data structured in the form of graphs or networks. To apply traditional machine learning and data analytic techniques to such data it is necessary to transform graphs into vector‐based representations that preserve the most essential structural properties of graphs. For this purpose, a large number of graph embedding methods have been proposed in the literature. Most of them produce general‐purpose embeddings suitable for a variety of applications such as node clustering, node classification, graph visualization and link prediction. In this article, we propose two novel graph embedding algorithms based on random walks that are specifically designed for the node classification problem. Random walk sampling strategies of the proposed algorithms have been designed to pay special attention to hubs–high‐degree nodes that have the most critical role for the overall connectedness in large‐scale graphs. The proposed methods are experimentally evaluated by analyzing the classification performance of three classification algorithms trained on embeddings of real‐world networks. The obtained results indicate that our methods considerably improve the predictive power of examined classifiers compared with currently the most popular random walk method for generating general‐purpose graph embeddings (node2vec).

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Section: Introductionmentioning

confidence: 99%