@inproceedings{, author = {Khan, Rayyan Ahmad and Anwaar, Muhammad Umer and Kaddah, Omran and Han, Zhiwei and Kleinsteuber, Martin}, title = {Unsupervised Learning of Joint Embeddings for Node Representation and Community Detection}, booktitle = {the Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference (2021)}, year = {2021}, abstract = {In this paper, we study how to simultaneously learn two highly correlated tasks of graph analysis, i.e., community detection and node representation learning. We propose an efficient generative model called VECoDeR for jointly learning Variational Embeddings for Community Detection and node Representation. VECoDeR assumes that every node can be a member of one or more communities. The node embeddings are learned in such a way that connected nodes are not only "closer" to each other but also share similar community assignments. A joint learning framework leverages community-aware node embeddings for better community detection. We demonstrate on several graph datasets that VECoDeR effectively out-performs many competitive baselines on all three tasks i.e. node classification, overlapping community detection and non-overlapping community detection. We also show that VECoDeR is computationally efficient and has quite robust performance with varying hyperparameters.}, url = {https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/sub_354.pdf}, } @incollection{, author = {Han, Zhiwei and Anwaar, Muhammad Umer and Arumugaswamy, Shyam and Weber, Thomas and Qiu, Tianming and Shen, Hao and Liu, Yuanting and Kleinsteuber, Martin}, title = {Metapath- and Entity-aware Graph Neural Network for Recommendation}, booktitle = {Arxiv}, year = {2020}, abstract = {In graph neural networks (GNNs), message passing iteratively aggregates nodes' information from their direct neighbors while neglecting the sequential nature of multi-hop node connections. Such sequential node connections e.g., metapaths, capture critical insights for downstream tasks. Concretely, in recommender systems (RSs), disregarding these insights leads to inadequate distillation of collaborative signals. In this paper, we employ collaborative subgraphs (CSGs) and metapaths to form metapath-aware subgraphs, which explicitly capture sequential semantics in graph structures. We propose meta-path and entity awared graph neural network, which trains multilayer GNNs to perform metapath-aware information aggregation on such subgraphs. This aggregated information from different metapaths is then fused using attention mechanism. Finally, PEAGNN gives us the representations for node and subgraph, which can be used to train MLP for predicting score for target user-item pairs. To leverage the local structure of CSGs, we present entity-awareness that acts as a contrastive regularizer on node embedding. Moreover, PEAGNN can be combined with prominent layers such as GAT, GCN and GraphSage. Our empirical evaluation shows that our proposed technique outperforms competitive baselines on several datasets for recommendation tasks. Further analysis demonstrates that PEAGNN also learns meaningful metapath combinations from a given set of metapaths.}, howpublished = {preprint}, url = {https://arxiv.org/abs/2010.11793}, }