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Embedding Networks: Node and Graph Level Representations
Series: M.Tech (Research) Thesis Defence - ONLINE
Speaker: Ms. Manasvi AggarwalM.Tech (Research) StudentDept. of CSA
Date/Time: Oct 28 11:00:00
Location: Microsoft Teams - ON-LINE
Faculty Advisor: Prof. M Narasimha Murty & Prof. Shalabh Bhatnagar
Network representation learning is essential to carry out various network analysis tasks. Graphs are the most suitable structures to represent real-world relational data such as social networks and molecular structures. In this thesis work, we focus on learning representations of the nodes and the entire graphs. Graph neural networks gained significant attention for graph representation and classification.
For graph classification, existing pooling approaches do not consider both the region-based and the graph level importance of the nodes together. We address this issue in the first part of the thesis by proposing a novel graph pooling layer R2POOL, which retains the most informative nodes for the next coarser version of the graph. Further, we integrate R2POOL with our multi-level prediction and branch training strategies to learn graph representations and to further enhance the model's capability for graph classification.
Moreover, the attention mechanisms on graphs improve the performance of graph neural networks. Typically, it helps to identify a neighbor node which plays a more important role in determining the label of the node under consideration. But in the real-world situation, a particular subset of nodes together may be significant. In the second part of the thesis, we address this problem and introduce the concept of subgraph attention for graphs. To show the efficiency of this scheme, we use subgraph attention for node classification.
Additionally, the hierarchical graph pooling is promising in graph literature. But, not all the graphs at different levels play an equal role in graph classification. Towards this end, we propose a novel algorithm called SubGattPool, which jointly learns the subgraph attention and employs two different attention mechanisms to find the important nodes in a hierarchy and the individual hierarchies in a graph for embedding and classifying the graphs given a collection of graphs. Improved performance over the state-of-the-art on both the real world and synthetic graphs for node and graph classification shows the efficiency of the proposed algorithms.
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