Collaborative Knowledge-Enhanced Recommendation with Self-Supervisions

Knowledge-enhanced recommendation (KER) aims to integrate the knowledge graph (KG) into collaborative filtering (CF) for alleviating the sparsity and cold start problems. The state-of-the-art graph neural network (GNN)–based methods mainly focus on exploiting the connectivity between entities in the knowledge graph, while neglecting the interaction relation between items reflected in the user-item interactions. Moreover, the widely adopted BPR loss for model optimization fails to provide sufficient supervisions for learning discriminative representation of users and items. To address these issues, we propose the collaborative knowledge-enhanced recommendation (CKER) method. Specifically, CKER proposes a collaborative graph convolution network (CGCN) to learn the user and item representations from the connection between items in the constructed interaction graph and the connectivity between entities in the knowledge graph. Moreover, we introduce the self-supervised learning to maximize the mutual information between the interaction- and knowledge-aware user preferences by deriving additional supervision signals. We conduct comprehensive experiments on two benchmark datasets, namely Amazon-Book and Last-FM, and the experimental results show that CKER can outperform the state-of-the-art baselines in terms of recall and NDCG on knowledge-enhanced recommendation.

Automatic Narrative Summarization for Visualizing Cyber Security Logs and Incident Reports

Cyber security logs and incident reports describe a narrative, but in practice analysts view the data in tables where it can be difficult to follow the narrative. Narrative visualizations are useful, but common examples use a summarized narrative instead of the full story's narrative; it is unclear how to automatically generate these summaries. This paper presents (1) a narrative summarization algorithm to reduce the size and complexity of cyber security narratives with a user-customizable summarization level, and (2) a narrative visualization tailored for incident reports and network logs. An evaluation on real incident reports shows that the summarization algorithm reduces false positives and improves average precision by 41% while reducing average incident report size up to 79%. Together, the visualization and summarization algorithm generate compact representations of cyber narratives that earned praise from a SOC analyst. We further demonstrate that the summarization algorithm can apply to other types of dynamic graphs by automatically generating a summary of the Les Misérables character interaction graph. We find that the list of main characters in the automatically generated summary has substantial agreement with human-generated summaries. A version of this paper, data, and code is freely available at https://osf.io/ekzbp/.

Knowledge-Aware Dialogue Generation via Hierarchical Infobox Accessing and Infobox-Dialogue Interaction Graph Network

Due to limited knowledge carried by queries, traditional dialogue systems often face the dilemma of generating boring responses, leading to poor user experience. To alleviate this issue, this paper proposes a novel infobox knowledge-aware dialogue generation approach, HITA-Graph, with three unique features. First, open-domain infobox tables that describe entities with relevant attributes are adopted as the knowledge source. An order-irrelevance Hierarchical Infobox Table Encoder is proposed to represent an infobox table at three levels of granularity. In addition, an Infobox-Dialogue Interaction Graph Network is built to effectively integrate the infobox context and the dialogue context into a unified infobox representation. Second, a Hierarchical Infobox Attribute Attention mechanism is developed to access the encoded infobox knowledge at different levels of granularity. Last but not least, a Dynamic Mode Fusion strategy is designed to allow the Decoder to select a vocabulary word or copy a word from the given infobox/query. We extract infobox tables from Chinese Wikipedia and construct an infobox knowledge base. Extensive evaluation on an open-released Chinese corpus demonstrates the superior performance of our approach against several representative methods.

Hi–C interaction graph analysis reveals the impact of histone modifications in chromatin shape

Chromosome conformation capture experiments such as Hi–C map the three-dimensional spatial organization of genomes in a genome-wide scale. Even though Hi–C interactions are not biased towards any of the histone modifications, previous analysis has revealed denser interactions around many histone modifications. Nevertheless, simultaneous effects of these modifications in Hi–C interaction graph have not been fully characterized yet, limiting our understanding of genome shape. Here, we propose ChromatinCoverage and its extension TemporalPrizeCoverage methods to decompose Hi–C interaction graph in terms of known histone modifications. Both methods are based on set multicover with pairs, where each Hi–C interaction is tried to be covered by histone modification pairs. We find 4 histone modifications H3K4me1, H3K4me3, H3K9me3, H3K27ac to be significantly predictive of most Hi–C interactions across species, cell types and cell cycles. The proposed methods are quite effective in predicting Hi–C interactions and topologically-associated domains in one species, given it is trained on another species or cell types. Overall, our findings reveal the impact of subset of histone modifications in chromatin shape via Hi–C interaction graph.