12,854 research outputs found
SPGP: Structure Prototype Guided Graph Pooling
While graph neural networks (GNNs) have been successful for node
classification tasks and link prediction tasks in graph, learning graph-level
representations still remains a challenge. For the graph-level representation,
it is important to learn both representation of neighboring nodes, i.e.,
aggregation, and graph structural information. A number of graph pooling
methods have been developed for this goal. However, most of the existing
pooling methods utilize k-hop neighborhood without considering explicit
structural information in a graph. In this paper, we propose Structure
Prototype Guided Pooling (SPGP) that utilizes prior graph structures to
overcome the limitation. SPGP formulates graph structures as learnable
prototype vectors and computes the affinity between nodes and prototype
vectors. This leads to a novel node scoring scheme that prioritizes informative
nodes while encapsulating the useful structures of the graph. Our experimental
results show that SPGP outperforms state-of-the-art graph pooling methods on
graph classification benchmark datasets in both accuracy and scalability.Comment: 18 pages, 6 figure
Bilinear Graph Neural Network with Neighbor Interactions
Graph Neural Network (GNN) is a powerful model to learn representations and
make predictions on graph data. Existing efforts on GNN have largely defined
the graph convolution as a weighted sum of the features of the connected nodes
to form the representation of the target node. Nevertheless, the operation of
weighted sum assumes the neighbor nodes are independent of each other, and
ignores the possible interactions between them. When such interactions exist,
such as the co-occurrence of two neighbor nodes is a strong signal of the
target node's characteristics, existing GNN models may fail to capture the
signal. In this work, we argue the importance of modeling the interactions
between neighbor nodes in GNN. We propose a new graph convolution operator,
which augments the weighted sum with pairwise interactions of the
representations of neighbor nodes. We term this framework as Bilinear Graph
Neural Network (BGNN), which improves GNN representation ability with bilinear
interactions between neighbor nodes. In particular, we specify two BGNN models
named BGCN and BGAT, based on the well-known GCN and GAT, respectively.
Empirical results on three public benchmarks of semi-supervised node
classification verify the effectiveness of BGNN -- BGCN (BGAT) outperforms GCN
(GAT) by 1.6% (1.5%) in classification accuracy.Codes are available at:
https://github.com/zhuhm1996/bgnn.Comment: Accepted by IJCAI 2020. SOLE copyright holder is IJCAI (International
Joint Conferences on Artificial Intelligence), all rights reserve
Community detection with spiking neural networks for neuromorphic hardware
We present results related to the performance of an algorithm for community
detection which incorporates event-driven computation. We define a mapping
which takes a graph G to a system of spiking neurons. Using a fully connected
spiking neuron system, with both inhibitory and excitatory synaptic
connections, the firing patterns of neurons within the same community can be
distinguished from firing patterns of neurons in different communities. On a
random graph with 128 vertices and known community structure we show that by
using binary decoding and a Hamming-distance based metric, individual
communities can be identified from spike train similarities. Using bipolar
decoding and finite rate thresholding, we verify that inhibitory connections
prevent the spread of spiking patterns.Comment: Conference paper presented at ORNL Neuromorphic Workshop 2017, 7
pages, 6 figure
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