2026-09-21T02:45:42Z https://opus4.kobv.de/opus4-uni-passau/oai

oai:kobv.de-opus4-uni-passau:921 2025-08-13 bibliography:false doc-type:PhDThesis open_access ddc ddc:004

Graph Representation Learning for Social Networks Fatemeh Salehi Rizi 004 Online social networks provide a rich source of information about millions of users worldwide. However, due to sparsity and complex structure, analyzing these networks is quite challenging and expensive. Recently, graph embedding emerged to map networked data into low-dimensional representations, i.e. vector embeddings. These representations are fed into off-the-shelf machine learning algorithms to simplify and speed up graph analytic tasks. Given the immense importance of social network analysis, in this thesis, we aim to study graph embedding for social networks in three directions. Firstly, we focus on social networks at microscopic level to primarily encode the structural characteristic of users' personal networks so-called ego networks. These representations are utilized in evaluation tasks whose performance depends on relational information from direct neighbors. For example, social circle prediction and event attendance inference both need structural information from neighbors in social networks. Secondly, we explore assessing the content of vector embeddings in terms of topological properties. This could be explained via two proposed approaches: 1) a learning to rank algorithm in which the model weights reveal the importance of properties at subgraph level (ego networks), 2) a regression model for direct approximation of network statistical properties at vertex level. Thirdly, we propose extensions of graph embedding to capture sign or additional content of social networks. Users in social media often express their feelings and attitudes towards others which forms sentiment links besides social links. We design a joint objective function whose terms capture semantics of both social and sentiment links simultaneously. We also propose a multi-task learning framework for networks with attributes and labels by stacking autoencoders. The weights of the learning tasks are automatically assigned via an adaptive loss weighting layer. Universität Passau Passau Innstrasse 29, 94032 Passau Michael Granitzer 2021-06-29 2021-07-26 PhDThesis Text urn:nbn:de:bvb:739-opus4-9211 application/pdf eng Standardbedingung laut Einverständniserklärung thesis.doctoral Universität Passau Passau Fakultät für Informatik und Mathematik 1 https://opus4.kobv.de/opus4-uni-passau/oai/container/index/docId/921 https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/921