pwc-archive/papers-with-abstracts huggingface dataset:
FraudJudger: Real-World Data Oriented Fraud Detection on Digital Payment Platforms
Authors: Ruoyu Deng, Na Ruan
Date: 2019-09-05 00:00:00
Tasks: Fraud Detection

Automated fraud behaviors detection on electronic payment platforms is a tough problem. Fraud users often exploit the vulnerability of payment platforms and the carelessness of users to defraud money, steal passwords, do money laundering, etc, which causes enormous losses to digital payment platforms and users. There are many challenges for fraud detection in practice. Traditional fraud detection methods require a large-scale manually labeled dataset, which is hard to obtain in reality. Manually labeled data cost tremendous human efforts. Besides, the continuous and rapid evolution of fraud users makes it hard to find new fraud patterns based on existing detection rules. In our work, we propose a real-world data oriented detection paradigm which can detect fraud users and upgrade its detection ability automatically. Based on the new paradigm, we design a novel fraud detection model, FraudJudger, to analyze users behaviors on digital payment platforms and detect fraud users with fewer labeled data in training. FraudJudger can learn the latent representations of users from unlabeled data with the help of Adversarial Autoencoder (AAE). Furthermore, FraudJudger can find new fraud patterns from unknown users by cluster analysis. Our experiment is based on a real-world electronic payment dataset. Comparing with other well-known fraud detection methods, FraudJudger can achieve better detection performance with only 10% labeled data.
A Persistent Weisfeiler–Lehman Procedure for Graph Classification
Authors: Bastian Rieck, Christian Bock, Karsten Borgwardt
Date: 2019-06-09 00:00:00
Proceeding: Proceedings of the 36th International Conference on Machine Learning 2019 6
Tasks: Classification, General Classification, Graph Classification, Topological Data Analysis

The Weisfeiler–Lehman graph kernel exhibits competitive performance in many graph classification tasks. However, its subtree features are not able to capture connected components and cycles, topological features known for characterising graphs. To extract such features, we leverage propagated node label information and transform unweighted graphs into metric ones. This permits us to augment the subtree features with topological information obtained using persistent homology, a concept from topological data analysis. Our method, which we formalise as a generalisation of Weisfeiler–Lehman subtree features, exhibits favourable  classification accuracy and its improvements in predictive performance are mainly driven by including cycle information.
Deep Graph Kernels
Authors: Pinar Yanardag, S. V. N. Vishwanathan
Date: 2015-08-10 00:00:00
Proceeding: KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2015 8
Tasks: Graph Classification, Language Modeling, Language Modelling

In this paper, we present Deep Graph Kernels (DGK), a unified framework to learn latent representations of sub-structures for graphs, inspired by latest advancements in language modeling and deep learning. Our framework leverages the dependency information between sub-structures by learning their latent representations. We demonstrate instances of our framework on three popular graph kernels, namely Graphlet kernels, Weisfeiler-Lehman subtree kernels, and Shortest-Path graph kernels. Our experiments on several benchmark datasets show that Deep Graph Kernels achieve significant improvements in classification accuracy over state-of-the-art graph kernels.
Efficient graphlet kernels for large graph comparison
Authors: Nino Shervashidze, S. V. N. Vishwanathan, Tobias Petri, Kurt Mehlhorn, Karsten Borgwardt.
Date: 2009-04-16 00:00:00
Proceeding: Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics 2009 4
Tasks: Graph Classification

State-of-the-art graph kernels do not scale to large graphs with hundreds of nodes and thousands of edges. In this article we propose to compare graphs by counting graphlets, i.e., subgraphs with k nodes where k ε {3; 4; 5g}. Exhaustive enumeration of all graphlets being prohibitively expensive, we introduce two theoretically grounded speedup schemes, one based on sampling and the second one specifically designed for bounded degree graphs. In our experimental evaluation, our novel kernels allow us to efficiently compare large graphs that cannot be tackled by existing graph kernels.
Distinguishing Enzyme Structures from Non-enzymes Without Alignments
Authors: Paul D.Dobson, Andrew J.Doig
Date: 2003-07-18 00:00:00
Proceeding: Journal of Molecular Biology 2003 7
Tasks: Graph Classification

The ability to predict protein function from structure is becoming increasingly important as the number of structures resolved is growing more rapidly than our capacity to study function. Current methods for predicting protein function are mostly reliant on identifying a similar protein of known function. For proteins that are highly dissimilar or are only similar to proteins also lacking functional annotations, these methods fail. Here, we show that protein function can be predicted as enzymatic or not without resorting to alignments. We describe 1178 high-resolution proteins in a structurally non-redundant subset of the Protein Data Bank using simple features such as secondary-structure content, amino acid propensities, surface properties and ligands. The subset is split into two functional groupings, enzymes and non-enzymes. We use the support vector machine-learning algorithm to develop models that are capable of assigning the protein class. Validation of the method shows that the function can be predicted to an accuracy of 77% using 52 features to describe each protein. An adaptive search of possible subsets of features produces a simplified model based on 36 features that predicts at an accuracy of 80%. We compare the method to sequence-based methods that also avoid calculating alignments and predict a recently released set of unrelated proteins. The most useful features for distinguishing enzymes from non-enzymes are secondary-structure content, amino acid frequencies, number of disulphide bonds and size of the largest cleft. This method is applicable to any structure as it does not require the identification of sequence or structural similarity to a protein of known function.
Quantum-based subgraph convolutional neural networks
Authors: Zhihong Zhang, Dong-Dong Chen, Jianjia Wang, Lu Bai, Edwin R. Hancock
Date: 2019-04-01 00:00:00
Proceeding: Pattern Recognition 2019 4
Tasks: General Classification, Graph Classification, Node Classification

This paper proposes a new graph convolutional neural network architecture based on a depth-based representation of graph structure deriving from quantum walks, which we refer to as the quantum-based subgraph convolutional neural network (QS-CNNs). This new architecture captures both the global topological structure and the local connectivity structure within a graph. Specifically, we commence by establishing a family of K-layer expansion subgraphs for each vertex of a graph by quantum walks, which captures the global topological arrangement information for substructures contained within a graph. We then design a set of fixed-size convolution filters over the subgraphs, which helps to characterise multi-scale patterns residing in the data. The idea is to apply convolution filters sliding over the entire set of subgraphs rooted at a vertex to extract the local features analogous to the standard convolution operation on grid data. Experiments on eight graph-structured datasets demonstrate that QS-CNNs architecture is capable of outperforming fourteen state-of-the-art methods for the tasks of node classification and graph classification.
Graph Kernels Based on Linear Patterns: Theoretical and Experimental Comparisons
Authors: Linlin Jia, Benoit Gaüzère, Paul Honeine
Date: 2019-03-01 00:00:00
Proceeding: Pattern Recognition 2019 3
Tasks: BIG-bench Machine Learning, Graph Classification

Graph kernels are powerful tools to bridge the gap between machine learning and data encoded as graphs. Most graph kernels are based on the decomposition of graphs into a set of patterns. The similarity between two graphs is then deduced from the similarity between corresponding patterns. Kernels based on linear patterns constitute a good trade-off between accuracy performance and computational complexity. In this work, we propose a thorough investigation and comparison of graph kernels based on different linear patterns, namely walks and paths. First, all these kernels are explored in detail, including their mathematical foundations, structures of patterns and computational complexity. Then, experiments are performed on various benchmark datasets exhibiting different types of graphs, including labeled and unlabeled graphs, graphs with different numbers of vertices, graphs with different average vertex degrees, cyclic and acyclic graphs. Finally, for regression and classification tasks, performance and computational complexity of kernels are compared and analyzed, and suggestions are proposed to choose kernels according to the types of graph datasets. This work leads to a clear comparison of strengths and weaknesses of these kernels. An open-source Python library containing an implementation of all discussed kernels is publicly available on GitHub to the community, thus allowing to promote and facilitate the use of graph kernels in machine learning problems.
Propagation kernels: efficient graph kernels from propagated information
Authors: Marion Neumann, Roman Garnett, Christian Bauckhage, Kristian Kersting
Date: 2019-02-01 00:00:00
Proceeding: Machine Learning 2019 2
Tasks: Graph Classification

We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how information spreads through a set of given graphs. They leverage early-stage distributions from propagation schemes such as random walks to capture structural information encoded in node labels, attributes, and edge information. This has two benefits. First, off-the-shelf propagation schemes can be used to naturally construct kernels for many graph types, including labeled, partially labeled, unlabeled, directed, and attributed graphs. Second, by leveraging existing efficient and informative propagation schemes, propagation kernels can be considerably faster than state-of-the-art approaches without sacrificing predictive performance. We will also show that if the graphs at hand have a regular structure, for instance when modeling image or video data, one can exploit this regularity to scale the kernel computation to large databases of graphs with thousands of nodes. We support our contributions by exhaustive experiments on a number of real-world graphs from a variety of application domains.
SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network
Authors: Asma Atamna, Nataliya Sokolovska, Jean-Claude Crivello
Date: 2019-04-08 00:00:00
Proceeding: HAL archives-ouvertes 2019 4
Tasks: BIG-bench Machine Learning, Deep Learning, General Classification, Graph Classification, Graph Similarity

A wide range of machine learning problems involve handling graph-structured data. Existing machine learning approaches for graphs, however, often imply computing expensive graph similarity measures, preprocessing input graphs, or explicitly ordering graph nodes. In this work, we present a novel and simple convolutional neural network architecture for supervised learning on graphs that is provably invariant to node permutation. The proposed architecture operates directly on arbitrary graphs and performs no node sorting. It also uses a simple multi-layer perceptron for prediction as opposed to conventional convolution layers commonly used in other deep learning approaches for graphs. Despite its simplicity, our architecture is competitive with state-of-the-art graph kernels and existing graph neural networks on benchmark graph classification data sets. Our approach clearly outperforms other deep learning algorithms for graphs on multiple multiclass classification tasks. We also evaluate our approach on a real-world original application in materials science, on which we achieve extremely reasonable results.
Graph Invariant Kernels
Authors: Francesco Orsini, Paolo Frasconi, Luc De Raedt
Date: 2015-07-25 00:00:00
Proceeding: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015) 2015 7
Tasks: Graph Classification

We introduce a novel kernel that upgrades the Weisfeiler-Lehman and other graph kernels to effectively exploit high-dimensional and continuous vertex attributes. Graphs are first decomposed into subgraphs. Vertices of the subgraphs are then compared by a kernel that combines the similarity of their labels and the similarity of their structural role, using a suitable vertex invariant. By changing this invariant we obtain a family of graph kernels which includes generalizations of Weisfeiler-Lehman, NSPDK, and propagation kernels. We demonstrate empirically that these kernels obtain state-of-the-art results on relational data sets.
hpGAT: High-order Proximity Informed Graph Attention Network
Authors: Zhining Liu, Weiyi Liu, Pin-Yu Chen, Chenyi Zhuang, Chengyun Song
Date: 2019-08-28 00:00:00
Proceeding: IEEE Access 2019 8
Tasks: Graph Attention, Node Classification, Vocal Bursts Intensity Prediction

Graph neural networks (GNNs) have recently made remarkable breakthroughs in the paradigm of learning with graph-structured data. However, most existing GNNs limit the receptive field of the node on each layer to its connected (one-hop) neighbors, which disregards the fact that large receptive field has been proven to be a critical factor in state-of-the-art neural networks. In this paper, we propose a novel approach to appropriately define a variable receptive field for GNNs by incorporating high-order proximity information extracted from the hierarchical topological structure of the input graph. Specifically, multiscale groups obtained from trainable hierarchical semi-nonnegative matrix factorization are used for adjusting the weights when aggregating one-hop neighbors. Integrated with the graph attention mechanism on attributes of neighboring nodes, the learnable parameters within the process of aggregation are optimized in an end-to-end manner. Extensive experiments show that the proposed method (hpGAT) outperforms state-of-the-art methods and demonstrate the importance of exploiting high-order proximity in handling noisy information of local neighborhood.
Jointly learning heterogeneous features for rgb-d activity recognition
Authors: Jian-Fang Hu, Wei-Shi Zheng, Jian-Huang Lai, Jian-Guo Zhang
Date: 2016-12-15 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence ( Volume: 39 , Issue: 11 , Nov. 1 2017 ) 2016 12
Tasks: Activity Recognition, Benchmarking, Human-Object Interaction Detection, Multi-Task Learning, Skeleton Based Action Recognition

In this paper, we focus on heterogeneous features learning for RGB-D activity recognition. We find that features from different channels (RGB, depth) could share some similar hidden structures, and then propose a joint learning model to simultaneously explore the shared and feature-specific components as an instance of heterogeneous multi-task learning. The proposed model formed in a unified framework is capable of: 1) jointly mining a set of subspaces with the same dimensionality to exploit latent shared features across different feature channels, 2) meanwhile, quantifying the shared and feature-specific components of features in the subspaces, and 3) transferring feature-specific intermediate transforms (i-transforms) for learning fusion of heterogeneous features across datasets. To efficiently train the joint model, a three-step iterative optimization algorithm is proposed, followed by a simple inference model. Extensive experimental results on four activity datasets have demonstrated the efficacy of the proposed method. Anew RGB-D activity dataset focusing on human-object interaction is further contributed, which presents more challenges for RGB-D activity benchmarking.
Graph Based Skeleton Modeling for Human Activity Analysis
Authors: Jiun-Yu Kao, Antonio Ortega, Dong Tian, Hassan Mansour, Anthony Vetro
Date: 2019-08-26 00:00:00
Proceeding: 2019 IEEE International Conference on Image Processing (ICIP) 2019 8
Tasks: Action Recognition, graph construction, Skeleton Based Action Recognition

Understanding human activity based on sensor information is required in many applications and has been an active research area. With the advancement of depth sensors and tracking algorithms, systems for human motion activity analysis can be built by combining off-the-shelf motion tracking systems with application-dependent learning tools to extract higher semantic level information. Many of these motion tracking systems provide raw motion data registered to the skeletal joints in the human body. In this paper, we propose novel representations for human motion data using the skeleton-based graph structure along with techniques in graph signal processing. Methods for graph construction and their corresponding basis functions are discussed. The proposed representations can achieve comparable classification performance in action recognition tasks while additionally being more robust to noise and missing data.
CensNet: Convolution with Edge-Node Switching in Graph Neural Networks
Authors: Xiaodong Jiang, Pengsheng Ji, Sheng Li
Date: 2019-08-10 00:00:00
Proceeding: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) 2019 8
Tasks: Graph Classification, Graph Embedding, Graph Neural Network, Graph Regression, Node Classification, regression

In this paper, we present CensNet, Convolution with Edge-Node Switching graph neural network, for semi-supervised classification and regression in graph-structured data with both node and edge features. CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space. By using line graph of the original undirected graph, the role of nodes and edges are switched, and two novel graph convolution operations are proposed for feature propagation. Experimental results on real-world academic citation networks and quantum chemistry graphs show that our approach has achieved or matched the state-of-the-art performance.
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
Authors: Xiangnan He, Hanwang Zhang, Min-Yen Kan, Tat-Seng Chua
Date: 2017-08-16 00:00:00

This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due to the large space of unobserved feedback, most existing works resort to assign a uniform weight to the missing data to reduce computational complexity. However, such a uniform assumption is invalid in real-world settings. Second, most methods are also designed in an offline setting and fail to keep up with the dynamic nature of online data. We address the above two issues in learning MF models from implicit feedback. We first propose to weight the missing data based on item popularity, which is more effective and flexible than the uniform-weight assumption. However, such a non-uniform weighting poses efficiency challenge in learning the model. To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data. We exploit this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback. Through comprehensive experiments on two public datasets in both offline and online protocols, we show that our eALS method consistently outperforms state-of-the-art implicit MF methods. Our implementation is available at https://github.com/hexiangnan/sigir16-eals.
Molecule Property Prediction Based on Spatial Graph Embedding
Authors: Xiao-Feng Wang, Zhen Li, Mingjian Jiang, Shuang Wang, Shugang Zhang, Zhiqiang Wei
Date: 2019-08-22 00:00:00
Proceeding: Journal of Chemical Information and Modeling 2019 8
Tasks: Drug Discovery, Graph Embedding, Graph Regression, Prediction, Property Prediction, Representation Learning

Accurate prediction of molecular properties is important for new compound design, which is a crucial step in drug discovery. In this paper, molecular graph data is utilized for property prediction based on graph convolution neural networks. In addition, a convolution spatial graph embedding layer (C-SGEL) is introduced to retain the spatial connection information on molecules. And, multiple C-SGELs are stacked to construct a convolution spatial graph embedding network (C-SGEN) for end-to-end representation learning. In order to enhance the robustness of the network, molecular fingerprints are also combined with C-SGEN to build a composite model for predicting molecular properties. Our comparative experiments have shown that our method is accurate and achieves the best results on some open benchmark datasets.
EEG emotion recognition using dynamical graph convolutional neural networks
Authors: Zhenyang Zhang, Wenming Zheng, Peng Song, Zhen Cui
Date: 2018-03-21 00:00:00
Proceeding: IEEE Transactions on Affective Computing 2018 3
Tasks: EEG, EEG Emotion Recognition, Electroencephalogram (EEG), Emotion Classification, Emotion Recognition

In this paper, a multichannel EEG emotion recognition method based on a novel dynamical graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed EEG emotion recognition method is to use a graph to model the multichannel EEG features and then perform EEG emotion classification based on this model. Different from the traditional graph convolutional neural networks (GCNN) methods, however, the proposed DGCNN method can dynamically learn the intrinsic relationship between different electroencephalogram (EEG) channels, represented by an adjacency matrix, via training a neural network so as to benefit for more discriminative EEG feature extraction. Then, the learned adjacency matrix is used for learning more discriminative features for improving the EEG emotion recognition. We conduct extensive experiments on the SJTU emotion EEG dataset (SEED) and DREAMER dataset. The experimental results demonstrate that the proposed method achieves better recognition performance than the state-of-the-art methods, in which the average recognition accuracy of 90.4\% is achieved for subject dependent experiment while 79.95\% for subject independent cross-validation one on the SEED database, and the average accuracies of 86.23\%, 84.54\% and 85.02\% are respectively obtained for valence, arousal and dominance classifications on the DREAMER database.