Adaptive Multi-grained Graph Neural Networks

Graph Neural Networks (GNNs) have been increasingly deployed in a multitude of different applications that involve node-wise and graph-level tasks. The existing literature usually studies these questions independently while they are inherently correlated. We propose in this work a unified model, Adaptive Multi-grained GNN (AdamGNN), to learn node and graph level representation interactively. Compared with the existing GNN models and pooling methods, AdamGNN enhances node representation with multi-grained semantics and avoids node feature and graph structure information loss during pooling. More specifically, a differentiable pooling operator in AdamGNN is used to obtain a multi-grained structure that involves node-wise and meso/macro level semantic information. The unpooling and flyback aggregators in AdamGNN is to leverage the multi-grained semantics to enhance node representation. The updated node representation can further enrich the generated graph representation in the next iteration. Experimental results on twelve real-world graphs demonstrate the effectiveness of AdamGNN on multiple tasks, compared with several competing methods. In addition, the ablation and empirical studies confirm the effectiveness of different components in AdamGNN

Hierarchical Message-Passing Graph Neural Networks

Graph Neural Networks (GNNs) have become a promising approach to machine learning with graphs. Since existing GNN models are based on flat message-passing mechanisms, two limitations need to be tackled. One is costly in encoding global information on the graph topology. The other is failing to model meso- and macro-level semantics hidden in the graph, such as the knowledge of institutes and research areas in an academic collaboration network. To deal with these two issues, we propose a novel Hierarchical Message-Passing Graph Neural Networks framework. The main idea is to generate a hierarchical structure that re-organises all nodes in a graph into multi-level clusters, along with intra- and inter-level edge connections. The derived hierarchy not only creates shortcuts connecting far-away nodes so that global information can be efficiently accessed via message passing but also incorporates meso- and macro-level semantics into the learning of node embedding. We present the first model to implement this hierarchical message-passing mechanism, termed Hierarchical Community-aware Graph Neural Network (HC-GNN), based on hierarchical communities detected from the graph. Experiments conducted on eight datasets under transductive, inductive, and few-shot settings exhibit that HC-GNN can outperform state-of-the-art GNN models in network analysis tasks, including node classification, link prediction, and community detection

Do strange stars exist in the Universe?

Definitely, an affirmative answer to this question would have implications of fundamental importance for astrophysics (a new class of compact stars), and for the physics of strong interactions (deconfined phase of quark matter, and strange matter hypothesis). In the present work, we use observational data for the newly discovered millisecond X-ray pulsar SAX J1808.4-3658 and for the atoll source 4U 1728-34 to constrain the radius of the underlying compact stars. Comparing the mass-radius relation of these two compact stars with theoretical models for both neutron stars and strange stars, we argue that a strange star model is more consistent with SAX J1808.4-3658 and 4U 1728-34, and suggest that they are likely strange star candidates.Comment: In memory of Bhaskar Datta. -- Invited talk at the Pacific Rim Conference on Stellar Astrophysics (Hong Kong, aug. 1999