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1. Dynamic Relevance Graph Network for Knowledge-Aware Question Answering

   Zheng, Chen; Kordjamshidi, Parisa (October 2022, Proceedings of the 29th International Conference on Computational Linguistics)

   This work investigates the challenge of learning and reasoning for Commonsense Question Answering given an external source of knowledge in the form of a knowledge graph (KG). We propose a novel graph neural network architecture, called Dynamic Relevance Graph Network (DRGN). DRGN operates on a given KG subgraph based on the question and answers entities and uses the relevance scores between the nodes to establish new edges dynamically for learning node representations in the graph network. This explicit usage of relevance as graph edges has the following advantages, a) the model can exploit the existing relationships, re-scale the node weights, and influence the way the neighborhood nodes’ representations are aggregated in the KG subgraph, b) It potentially recovers the missing edges in KG that are needed for reasoning. Moreover, as a byproduct, our model improves handling the negative questions due to considering the relevance between the question node and the graph entities. Our proposed approach shows competitive performance on two QA benchmarks, CommonsenseQA and OpenbookQA, compared to the state-of-the-art published results. 

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2. Visual analysis of multi-omics data

   https://doi.org/10.3389/fbinf.2024.1395981  

   Swart, Austin; Caspi, Ron; Paley, Suzanne; Karp, Peter D (September 2024, Frontiers in Bioinformatics)

   We present a tool for multi-omics data analysis that enables simultaneous visualization of up to four types of omics data on organism-scale metabolic network diagrams. The tool’s interactive web-based metabolic charts depict the metabolic reactions, pathways, and metabolites of a single organism as described in a metabolic pathway database for that organism; the charts are constructed using automated graphical layout algorithms. The multi-omics visualization facility paints each individual omics dataset onto a different “visual channel” of the metabolic-network diagram. For example, a transcriptomics dataset might be displayed by coloring the reaction arrows within the metabolic chart, while a companion proteomics dataset is displayed as reaction arrow thicknesses, and a complementary metabolomics dataset is displayed as metabolite node colors. Once the network diagrams are painted with omics data, semantic zooming provides more details within the diagram as the user zooms in. Datasets containing multiple time points can be displayed in an animated fashion. The tool will also graph data values for individual reactions or metabolites designated by the user. The user can interactively adjust the mapping from data value ranges to the displayed colors and thicknesses to provide more informative diagrams. 

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3. Weak Supervision Network Embedding for Constrained Graph Learning

   Guo, Ting; Zhu, Xingquan; Wang, Yang; Chen, Fang (May 2021, Proc. of the Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science)

   Karlapalem, Kamal; Cheng, Hong; Ramakrishnan, Naren; null; null; Reddy, P. Krishna; Srivastava, Jaideep; Chakraborty, Tanmoy (Ed.)

   Constrained learning, a weakly supervised learning task, aims to incorporate domain constraints to learn models without requiring labels for each instance. Because weak supervision knowledge is useful and easy to obtain, constrained learning outperforms unsupervised learning in performance and is preferable than supervised learning in terms of labeling costs. To date, constrained learning, especially constrained clustering, has been extensively studied, but was primarily focused on data in the Euclidean space. In this paper, we propose a weak supervision network embedding (WSNE) for constrained learning of graphs. Because no label is available for individual nodes, we propose a new loss function to quantify the constraint-based loss, and integrate this loss in a graph convolutional neural network (GCN) and variational graph auto-encoder (VGAE) combined framework to jointly model graph structures and node attributes. The joint optimization allows WSNE to learn embedding not only preserving network topology and content, but also satisfying the constraints. Experiments show that WSNE outperforms baselines for constrained graph learning tasks, including constrained graph clustering and constrained graph classification. 

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4. Building the Collaboration Graph of Open-Source Software Ecosystem

   https://doi.org/10.1109/MSR52588.2021.00086  

   Lyulina, Elena; Jahanshahi, Mahmoud (May 2021, 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR))

   null (Ed.)

   The Open-Source Software community has become the center of attention for many researchers, who are investigating various aspects of collaboration in this extremely large ecosystem. Due to its size, it is difficult to grasp whether or not it has structure, and if so, what it may be. Our hackathon project aims to facilitate the understanding of the developer collaboration structure and relationships among projects based on the bi-graph of what projects developers contribute to by providing an interactive collaboration graph of this ecosystem, using the data obtained from World of Code [1] infrastructure. Our attempts to visualize the entirety of projects and developers were stymied by the inability of the layout and visualization tools to process the exceedingly large scale of the full graph. We used WoC to filter the nodes (developers and projects) and edges (developer contributions to a project) to reduce the scale of the graph that made it amenable to an interactive visualization and published the resulting visualizations. We plan to apply hierarchical approaches to be able to incorporate the entire data in the interactive visualizations and also to evaluate the utility of such visualizations for several tasks. 

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5. Drawing Graphs on the Sphere

   https://doi.org/10.1145/3399715.3399915  

   Perry, S; Gray, K; Yin, M; Kobourov, S (October 2020, 13th International Conference on Advanced Visual Interfaces (AVI))

   Graphs are most often visualized in the two dimensional Euclidean plane, but spherical space offers several advantages when visualizing graphs. First, some graphs such as skeletons of three dimensional polytopes (tetrahedron, cube, icosahedron) have spherical realizations that capture their 3D structure, which cannot be visualized as well in the Euclidean plane. Second, the sphere makes possible a natural “focus + context" visualization with more detail in the center of the view and less details away from the center. Finally, whereas layouts in the Euclidean plane implicitly define notions of “central" and “peripheral" nodes, this issue is reduced on the sphere, where the layout can be centered at any node of interest. We first consider a projection-reprojection method that relies on transformations often seen in cartography and describe the implementation of this method in the GMap visualization system. This approach allows many different types of 2D graph visualizations, such as node-link diagrams, LineSets, BubbleSets and MapSets, to be converted into spherical web browser visualizations. Next we consider an approach based on spherical multidimensional scaling, which performs graph layout directly on the sphere. This approach supports node-link diagrams and GMap-style visualizations, rendered in the web browser using WebGL. 

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