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Brain graph representation learning serves as the fundamental technique forbrain diseases diagnosis. Great efforts from both the academic and industrialcommunities have been devoted to brain graph representation learning in recentyears. The isomorphic neural network (IsoNN) introduced recently canautomatically learn the existence of sub-graph patterns in brain graphs, whichis also the state-of-the-art brain graph representation learning method by thiscontext so far. However, IsoNN fails to capture the orientations of sub-graphpatterns, which may render the learned representations to be useless for manycases. In this paper, we propose a new Iso-CapsNet (Isomorphic Capsule Net)model by introducing the graph isomorphic capsules for effective brain graphrepresentation learning. Based on the capsule dynamic routing, besides thesubgraph pattern existence confidence scores, Iso-CapsNet can also learn othersub-graph rich properties, including position, size and orientation, forcalculating the class-wise digit capsules. We have compared Iso-CapsNet withboth classic and state-of-the-art brain graph representation approaches withextensive experiments on four brain graph benchmark datasets. The experimentalresults also demonstrate the effectiveness of Iso-CapsNet, which canout-perform the baseline methods with significant improvements.

Iso-CapsNet: Isomorphic Capsule Network for Brain Graph Representation Learning