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.
Knowledge graphs gained popularity in recent years and have been useful for concept visualization and contextual information retrieval in various applications. However, constructing a knowledge graph by scraping long and complex unstructured texts for a new domain in the absence of a well-defined ontology or an existing labeled entity-relation dataset is difficult. Domains such as cybersecurity education can harness knowledge graphs to create a student-focused interactive and learning environment to teach cybersecurity. Learning cybersecurity involves gaining the knowledge of different attack and defense techniques, system setup and solving multi-facet complex real-world challenges that demand adaptive learning strategies and cognitive engagement. However, there are no standard datasets for the cybersecurity education domain. In this research work, we present a bottom-up approach to curate entity-relation pairs and construct knowledge graphs and question-answering models for cybersecurity education. To evaluate the impact of our new learning paradigm, we conducted surveys and interviews with students after each project to find the usefulness of bot and the knowledge graphs. Our results show that students found these tools informative for learning the core concepts and they used knowledge graphs as a visual reference to cross check the progress that helped them complete the project tasks.
Knowledge graphs are graph-based data models which can represent real-time data that is constantly growing with the addition of new information. The question-answering systems over knowledge graphs (KGQA) retrieve answers to a natural language question from the knowledge graph. Most existing KGQA systems use static knowledge bases for offline training. After deployment, they fail to learn from unseen new entities added to the graph. There is a need for dynamic algorithms which can adapt to the evolving graphs and give interpretable results. In this research work, we propose using new auction algorithms for question answering over knowledge graphs. These algorithms can adapt to changing environments in real-time, making them suitable for offline and online training. An auction algorithm computes paths connecting an origin node to one or more destination nodes in a directed graph and uses node prices to guide the search for the path. The prices are initially assigned arbitrarily and updated dynamically based on defined rules. The algorithm navigates the graph from the high-price to the low-price nodes. When new nodes and edges are dynamically added or removed in an evolving knowledge graph, the algorithm can adapt by reusing the prices of existing nodes and assigning arbitrary prices to the new nodes. For subsequent related searches, the “learned” prices provide the means to “transfer knowledge” and act as a “guide: to steer it toward the lower-priced nodes. Our approach reduces the search computational effort by 60% in our experiments, thus making the algorithm computationally efficient. The resulting path given by the algorithm can be mapped to the attributes of entities and relations in knowledge graphs to provide an explainable answer to the query. We discuss some applications for which our method can be used.
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