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Embedding Networks: Node and Graph Level Representations

Series: M.Tech (Research) Colloquium - ONLINE

Speaker: Ms. Manasvi Aggarwal M.Tech (Research) Student Dept. of CSA

Date/Time: Jun 24 13:00:00

Location: Microsoft Teams - ONLINE

Faculty Advisor: Prof. M. Narasimha Murty

Network representation learning is important to carry out various network analysis downstream tasks. Graphs are the most suitable structures to represent relational data such as social networks and molecular structures. In this thesis work, we focus on learning representations of the nodes as well as of the entire graphs. Graph neural networks got significant importance for graph representation. Recently, attention mechanisms on graphs show promising results for classification tasks. Most of the attention mechanisms developed in graph literature use attention to derive the importance of a node or a pair of nodes for different tasks. But in the real world situation, calculating importance up to a pair of nodes is not adequate.
To address this problem, we introduce a novel GNN based approach, subgraph attention, to classify the nodes of a graph. On the other hand, the hierarchical graph pooling is promising in the recent literature. But, not all the hierarchies of a graph play an equal role for graph classification. Towards this end, we propose an algorithm called SubGattPool to find the important nodes in a hierarchy and the importance of individual hierarchies in a graph for embedding and classifying the graphs given a collection of graphs. Moreover, existing pooling approaches do not consider both the region based as well as the graph level importance of the nodes together. In the next research work, we solve this issue by proposing a novel pooling layer named R2pool which retains the most informative nodes for the next coarser version of the graph. Further, we integrate R2pool with our branch training strategy to learn coarse to fine representations and improve the model's capability for graph classification by exploiting multi-level prediction strategy. Thorough experimentation on both the real world and synthetic graphs shows the merit of the proposed algorithms over the state-of-the-art.
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