9,275 research outputs found
Spectrum-based deep neural networks for fraud detection
In this paper, we focus on fraud detection on a signed graph with only a
small set of labeled training data. We propose a novel framework that combines
deep neural networks and spectral graph analysis. In particular, we use the
node projection (called as spectral coordinate) in the low dimensional spectral
space of the graph's adjacency matrix as input of deep neural networks.
Spectral coordinates in the spectral space capture the most useful topology
information of the network. Due to the small dimension of spectral coordinates
(compared with the dimension of the adjacency matrix derived from a graph),
training deep neural networks becomes feasible. We develop and evaluate two
neural networks, deep autoencoder and convolutional neural network, in our
fraud detection framework. Experimental results on a real signed graph show
that our spectrum based deep neural networks are effective in fraud detection
Learning to solve Minimum Cost Multicuts efficiently using Edge-Weighted Graph Convolutional Neural Networks
The minimum cost multicut problem is the NP-hard/APX-hard combinatorial optimization problem of partitioning a real-valued edge-weighted graph such as to minimize the total cost of the partition. While graph convolutional neural networks (GNN) have proven to be promising in the context of combinatorial optimization, most of them are only tailored to or tested on positive-valued edge weights, i.e. they do not comply to the nature of the multicut problem. We therefore adapt various GNN architectures including Graph Convolutional Networks, Signed Graph Convolutional Networks and Graph Isomorphic Networks to facilitate the efficient encoding of real-valued edge costs. Moreover, we employ a reformulation of the multicut ILP constraints to a polynomial program as loss function that allows to learn feasible multicut solutions in a scalable way. Thus, we provide the first approach towards end-to-end trainable multicuts. Our findings support that GNN approaches can produce good solutions in practice while providing lower computation times and largely improved scalability compared to LP solvers and optimized heuristics, especially when considering large instances
Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding
The problem of representing nodes in a signed network as low-dimensional
vectors, known as signed network embedding (SNE), has garnered considerable
attention in recent years. While several SNE methods based on graph
convolutional networks (GCN) have been proposed for this problem, we point out
that they significantly rely on the assumption that the decades-old balance
theory always holds in the real-world. To address this limitation, we propose a
novel GCN-based SNE approach, named as TrustSGCN, which corrects for incorrect
embedding propagation in GCN by utilizing the trustworthiness on edge signs for
high-order relationships inferred by the balance theory. The proposed approach
consists of three modules: (M1) generation of each node's extended ego-network;
(M2) measurement of trustworthiness on edge signs; and (M3)
trustworthiness-aware propagation of embeddings. Furthermore, TrustSGCN learns
the node embeddings by leveraging two well-known societal theories, i.e.,
balance and status. The experiments on four real-world signed network datasets
demonstrate that TrustSGCN consistently outperforms five state-of-the-art
GCN-based SNE methods. The code is available at
https://github.com/kmj0792/TrustSGCN.Comment: 12 pages, 8 figures, 9 table
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
Timely accurate traffic forecast is crucial for urban traffic control and
guidance. Due to the high nonlinearity and complexity of traffic flow,
traditional methods cannot satisfy the requirements of mid-and-long term
prediction tasks and often neglect spatial and temporal dependencies. In this
paper, we propose a novel deep learning framework, Spatio-Temporal Graph
Convolutional Networks (STGCN), to tackle the time series prediction problem in
traffic domain. Instead of applying regular convolutional and recurrent units,
we formulate the problem on graphs and build the model with complete
convolutional structures, which enable much faster training speed with fewer
parameters. Experiments show that our model STGCN effectively captures
comprehensive spatio-temporal correlations through modeling multi-scale traffic
networks and consistently outperforms state-of-the-art baselines on various
real-world traffic datasets.Comment: Proceedings of the 27th International Joint Conference on Artificial
Intelligenc
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