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Based on astrophysics observation, dark matter accounts for approximately 84% of all matter in the universe, with the remaining 16% being ordinary matter. According to the prevailing scientific model, dark matter particles are supersymmetric particles with a mass of 100 GeV (about the mass of 100 protons) with the density about 5000 particles m-3. Yet such a dominant substance dominates the universe, but one has not detected yet. Among various candidate, WIMPs are one of the most appealing one that plenty of facilities aim to detect. In this essay, the detection principles and some recent results of two representatives of the state-of-art mainstream detection methods were presented, i.e., the ‘Wukong’ astronomical satellite (DAMPE) and the Jinping Mountains of Sichuan Province, China (CJPL). It was shown that ‘Wukong’ has successfully detected an electron-positron pairs at an energy of 0.9 Tev. In Jinping, experiments are proposed to detect liquid argon dark matter. In this review, the two methods of detecting dark matter will be compared and the advantages, disadvantages will be analyzed. These results shed light on guiding further exploration of dark matter.

Since real-world objects and their interactions are often multi-modal and multi-typed, heterogeneous networks have been widely used as a more powerful, realistic, and generic superclass of traditional homogeneous networks (graphs). Meanwhile, representation learning (\aka~embedding) has recently been intensively studied and shown effective for various network mining and analytical tasks. In this work, we aim to provide a unified framework to deeply summarize and evaluate existing research on heterogeneous network embedding (HNE), which includes but goes beyond a normal survey. Since there has already been a broad body of HNE algorithms, as the first contribution of this work, we provide a generic paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms. Moreover, existing HNE algorithms, though mostly claimed generic, are often evaluated on different datasets. Understandable due to the application favor of HNE, such indirect comparisons largely hinder the proper attribution of improved task performance towards effective data preprocessing and novel technical design, especially considering the various ways possible to construct a heterogeneous network from real-world application data. Therefore, as the second contribution, we create four benchmark datasets with various properties regarding scale, structure, attribute/label availability, and \etc.~from different sources, towards handy and fair evaluations of HNE algorithms. As the third contribution, we carefully refactor and amend the implementations and create friendly interfaces for 13 popular HNE algorithms, and provide all-around comparisons among them over multiple tasks and experimental settings., Accepted by IEEE TKDE. All code and data available at https://github.com/yangji9181/HNE

In 2011, we proposed PathSim to systematically define and compute similarity between nodes in a heterogeneous information network (HIN), where nodes and links are from different types. In the PathSim paper, we for the first time introduced HIN with general network schema and proposed the concept of meta-paths to systematically define new relation types between nodes. In this paper, we summarize the impact of PathSim paper in both academia and industry. We start from the algorithms that are based on meta-path-based feature engineering, then move on to the recent development in heterogeneous network representation learning, including both shallow network embedding and heterogeneous graph neural networks. In the end, we make the connection between knowledge graphs and HINs and discuss the implication of meta-paths in the symbolic reasoning scenario. Finally, we point out several future directions.