Deep Graph Learning for Anomalous Citation Detection

• URL: http://arxiv.org/abs/2202.11360v1
• Date: Wed, 23 Feb 2022 09:05:28 GMT
• Title: Deep Graph Learning for Anomalous Citation Detection
• Authors: Jiaying Liu, Feng Xia, Xu Feng, Jing Ren, Huan Liu
• Abstract summary: We propose a novel deep graph learning model, namely GLAD (Graph Learning for Anomaly Detection), to identify anomalies in citation networks. Within the GLAD framework, we propose an algorithm called CPU (Citation PUrpose) to discover the purpose of citation based on citation texts.
• Score: 55.81334139806342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection is one of the most active research areas in various critical domains, such as healthcare, fintech, and public security. However, little attention has been paid to scholarly data, i.e., anomaly detection in a citation network. Citation is considered as one of the most crucial metrics to evaluate the impact of scientific research, which may be gamed in multiple ways. Therefore, anomaly detection in citation networks is of significant importance to identify manipulation and inflation of citations. To address this open issue, we propose a novel deep graph learning model, namely GLAD (Graph Learning for Anomaly Detection), to identify anomalies in citation networks. GLAD incorporates text semantic mining to network representation learning by adding both node attributes and link attributes via graph neural networks. It exploits not only the relevance of citation contents but also hidden relationships between papers. Within the GLAD framework, we propose an algorithm called CPU (Citation PUrpose) to discover the purpose of citation based on citation texts. The performance of GLAD is validated through a simulated anomalous citation dataset. Experimental results demonstrate the effectiveness of GLAD on the anomalous citation detection task.

Related papers

• Multitask Active Learning for Graph Anomaly Detection [48.690169078479116]
  We propose a novel MultItask acTIve Graph Anomaly deTEction framework, namely MITIGATE. By coupling node classification tasks, MITIGATE obtains the capability to detect out-of-distribution nodes without known anomalies. Empirical studies on four datasets demonstrate that MITIGATE significantly outperforms the state-of-the-art methods for anomaly detection.
  arXiv  Detail & Related papers  (2024-01-24T03:43:45Z)
• An Anchor Learning Approach for Citation Field Learning [23.507104046870186]
  We propose a novel algorithm, CIFAL, to boost the citation field learning performance. Experiments demonstrate that CIFAL outperforms state-of-the-art methods in citation field learning.
  arXiv  Detail & Related papers  (2023-09-07T08:42:40Z)
• BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection [50.26074811655596]
  We propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE) By swapping the context embeddings between nodes and edges, we enable the mutual detection of node and edge anomalies. BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs.
  arXiv  Detail & Related papers  (2023-07-28T00:44:57Z)
• Inline Citation Classification using Peripheral Context and Time-evolving Augmentation [23.88211560188731]
  We propose a new dataset, named 3Cext, which provides discourse information using the cited sentences. We propose PeriCite, a Transformer-based deep neural network that fuses peripheral sentences and domain knowledge.
  arXiv  Detail & Related papers  (2023-03-01T09:11:07Z)
• Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks [53.56901624204265]
  The present work deals with uncovering anomalous edges in attributed graphs using two distinct formulations with complementary strengths. The first relies on decomposing the graph data matrix into low rank plus sparse components to improve markedly performance. The second broadens the scope of the first by performing robust recovery of the unperturbed graph, which enhances the anomaly identification performance.
  arXiv  Detail & Related papers  (2021-04-17T20:00:40Z)
• Enhancing Scientific Papers Summarization with Citation Graph [78.65955304229863]
  We redefine the task of scientific papers summarization by utilizing their citation graph. We construct a novel scientific papers summarization dataset Semantic Scholar Network (SSN) which contains 141K research papers in different domains. Our model can achieve competitive performance when compared with the pretrained models.
  arXiv  Detail & Related papers  (2021-04-07T11:13:35Z)
• Be More with Less: Hypergraph Attention Networks for Inductive Text Classification [56.98218530073927]
  Graph neural networks (GNNs) have received increasing attention in the research community and demonstrated their promising results on this canonical task. Despite the success, their performance could be largely jeopardized in practice since they are unable to capture high-order interaction between words. We propose a principled model -- hypergraph attention networks (HyperGAT) which can obtain more expressive power with less computational consumption for text representation learning.
  arXiv  Detail & Related papers  (2020-11-01T00:21:59Z)
• Graph Neighborhood Attentive Pooling [0.5493410630077189]
  Network representation learning (NRL) is a powerful technique for learning low-dimensional vector representation of high-dimensional and sparse graphs. We propose a novel context-sensitive algorithm called GAP that learns to attend on different parts of a node's neighborhood using attentive pooling networks.
  arXiv  Detail & Related papers  (2020-01-28T15:05:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.