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Node Classification in Graph Data using Augmented Random Walk

عنوان مقاله: Node Classification in Graph Data using Augmented Random Walk
شناسه ملی مقاله: IRANWEB01_040
منتشر شده در اولین کنفرانس بین المللی وب پژوهی در سال 1394
مشخصات نویسندگان مقاله:

Hossein Rahmani - Department of Knowledge Engineering (DKE), Maastricht University, The Netherlands
Gerhard Weiss - Department of Knowledge Engineering (DKE), Maastricht University, The Netherlands

خلاصه مقاله:
Node classification in graph data plays an importantrole in web mining applications. We classify the existing nodeclassifiers into Inductive and Transductive approaches. Amongthe Transductive methods, the Majority Rule method (MRM) hasa prominent role. This method considers only the class labels ofthe neighboring nodes, neglecting the informative connectivityinformation in the graph data. In this paper, we propose anAugmented Random Walk (ARW) based approach to resolvemain limitations of MRM. In our proposed method, first, weaugment the initial graph by adding class labels as new nodes tothe graph and then we connect each classified node to itscorresponding class label nodes. Second, we apply a RandomWalk algorithm to find the similarity score of each un-classifiednode to different class labels. Third, we predict class labels withthe highest scores for the un-classified node. Empirical resultsshow that our proposed method clearly outperforms the MajorityRule method in six graph datasets with high homophily.

کلمات کلیدی:
Node Classification, Majority Rule, Graph Augmentation, Random Walk

صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/378234/