496 research outputs found
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
Graph Neural Networks (GNNs) have been widely applied to fraud detection
problems in recent years, revealing the suspiciousness of nodes by aggregating
their neighborhood information via different relations. However, few prior
works have noticed the camouflage behavior of fraudsters, which could hamper
the performance of GNN-based fraud detectors during the aggregation process. In
this paper, we introduce two types of camouflages based on recent empirical
studies, i.e., the feature camouflage and the relation camouflage. Existing
GNNs have not addressed these two camouflages, which results in their poor
performance in fraud detection problems. Alternatively, we propose a new model
named CAmouflage-REsistant GNN (CARE-GNN), to enhance the GNN aggregation
process with three unique modules against camouflages. Concretely, we first
devise a label-aware similarity measure to find informative neighboring nodes.
Then, we leverage reinforcement learning (RL) to find the optimal amounts of
neighbors to be selected. Finally, the selected neighbors across different
relations are aggregated together. Comprehensive experiments on two real-world
fraud datasets demonstrate the effectiveness of the RL algorithm. The proposed
CARE-GNN also outperforms state-of-the-art GNNs and GNN-based fraud detectors.
We integrate all GNN-based fraud detectors as an opensource toolbox:
https://github.com/safe-graph/DGFraud. The CARE-GNN code and datasets are
available at https://github.com/YingtongDou/CARE-GNN.Comment: Accepted by CIKM 202
Graph learning for anomaly analytics : algorithms, applications, and challenges
Anomaly analytics is a popular and vital task in various research contexts that has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks, like node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network, graph attention network, graph autoencoder, and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field. © 2023 Association for Computing Machinery
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges
Anomaly analytics is a popular and vital task in various research contexts,
which has been studied for several decades. At the same time, deep learning has
shown its capacity in solving many graph-based tasks like, node classification,
link prediction, and graph classification. Recently, many studies are extending
graph learning models for solving anomaly analytics problems, resulting in
beneficial advances in graph-based anomaly analytics techniques. In this
survey, we provide a comprehensive overview of graph learning methods for
anomaly analytics tasks. We classify them into four categories based on their
model architectures, namely graph convolutional network (GCN), graph attention
network (GAT), graph autoencoder (GAE), and other graph learning models. The
differences between these methods are also compared in a systematic manner.
Furthermore, we outline several graph-based anomaly analytics applications
across various domains in the real world. Finally, we discuss five potential
future research directions in this rapidly growing field
A Comprehensive Survey on Deep Graph Representation Learning
Graph representation learning aims to effectively encode high-dimensional
sparse graph-structured data into low-dimensional dense vectors, which is a
fundamental task that has been widely studied in a range of fields, including
machine learning and data mining. Classic graph embedding methods follow the
basic idea that the embedding vectors of interconnected nodes in the graph can
still maintain a relatively close distance, thereby preserving the structural
information between the nodes in the graph. However, this is sub-optimal due
to: (i) traditional methods have limited model capacity which limits the
learning performance; (ii) existing techniques typically rely on unsupervised
learning strategies and fail to couple with the latest learning paradigms;
(iii) representation learning and downstream tasks are dependent on each other
which should be jointly enhanced. With the remarkable success of deep learning,
deep graph representation learning has shown great potential and advantages
over shallow (traditional) methods, there exist a large number of deep graph
representation learning techniques have been proposed in the past decade,
especially graph neural networks. In this survey, we conduct a comprehensive
survey on current deep graph representation learning algorithms by proposing a
new taxonomy of existing state-of-the-art literature. Specifically, we
systematically summarize the essential components of graph representation
learning and categorize existing approaches by the ways of graph neural network
architectures and the most recent advanced learning paradigms. Moreover, this
survey also provides the practical and promising applications of deep graph
representation learning. Last but not least, we state new perspectives and
suggest challenging directions which deserve further investigations in the
future
Recommending on graphs: a comprehensive review from a data perspective
Recent advances in graph-based learning approaches have demonstrated their
effectiveness in modelling users' preferences and items' characteristics for
Recommender Systems (RSS). Most of the data in RSS can be organized into graphs
where various objects (e.g., users, items, and attributes) are explicitly or
implicitly connected and influence each other via various relations. Such a
graph-based organization brings benefits to exploiting potential properties in
graph learning (e.g., random walk and network embedding) techniques to enrich
the representations of the user and item nodes, which is an essential factor
for successful recommendations. In this paper, we provide a comprehensive
survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we
start from a data-driven perspective to systematically categorize various
graphs in GLRSs and analyze their characteristics. Then, we discuss the
state-of-the-art frameworks with a focus on the graph learning module and how
they address practical recommendation challenges such as scalability, fairness,
diversity, explainability and so on. Finally, we share some potential research
directions in this rapidly growing area.Comment: Accepted by UMUA
Detecting and Classifying Malevolent Dialogue Responses: Taxonomy, Data and Methodology
Conversational interfaces are increasingly popular as a way of connecting
people to information. Corpus-based conversational interfaces are able to
generate more diverse and natural responses than template-based or
retrieval-based agents. With their increased generative capacity of corpusbased
conversational agents comes the need to classify and filter out malevolent
responses that are inappropriate in terms of content and dialogue acts.
Previous studies on the topic of recognizing and classifying inappropriate
content are mostly focused on a certain category of malevolence or on single
sentences instead of an entire dialogue. In this paper, we define the task of
Malevolent Dialogue Response Detection and Classification (MDRDC). We make
three contributions to advance research on this task. First, we present a
Hierarchical Malevolent Dialogue Taxonomy (HMDT). Second, we create a labelled
multi-turn dialogue dataset and formulate the MDRDC task as a hierarchical
classification task over this taxonomy. Third, we apply stateof-the-art text
classification methods to the MDRDC task and report on extensive experiments
aimed at assessing the performance of these approaches.Comment: under review at JASIS
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA
Text-based visual question answering (TextVQA) faces the significant
challenge of avoiding redundant relational inference. To be specific, a large
number of detected objects and optical character recognition (OCR) tokens
result in rich visual relationships. Existing works take all visual
relationships into account for answer prediction. However, there are three
observations: (1) a single subject in the images can be easily detected as
multiple objects with distinct bounding boxes (considered repetitive objects).
The associations between these repetitive objects are superfluous for answer
reasoning; (2) two spatially distant OCR tokens detected in the image
frequently have weak semantic dependencies for answer reasoning; and (3) the
co-existence of nearby objects and tokens may be indicative of important visual
cues for predicting answers. Rather than utilizing all of them for answer
prediction, we make an effort to identify the most important connections or
eliminate redundant ones. We propose a sparse spatial graph network (SSGN) that
introduces a spatially aware relation pruning technique to this task. As
spatial factors for relation measurement, we employ spatial distance, geometric
dimension, overlap area, and DIoU for spatially aware pruning. We consider
three visual relationships for graph learning: object-object, OCR-OCR tokens,
and object-OCR token relationships. SSGN is a progressive graph learning
architecture that verifies the pivotal relations in the correlated object-token
sparse graph, and then in the respective object-based sparse graph and
token-based sparse graph. Experiment results on TextVQA and ST-VQA datasets
demonstrate that SSGN achieves promising performances. And some visualization
results further demonstrate the interpretability of our method.Comment: Accepted by TIP 202
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