Graph Representation Learning for Social Networks

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.

Level thesis:degree_level

thesis.doctoral

Grantor dc:publisher

Universität Passau

Year

2021

Author dc:creator

• Salehi Rizi, Fatemeh

Contributors dc:contributor

• Granitzer, Michael

Statement dc:rights

• Standardbedingung laut Einverständniserklärung

Repository record source_url

https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/921

OAI identifier oai:identifier

oai:kobv.de-opus4-uni-passau:921