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AMiner is a novel online academic search and mining system, and it aims to provide a systematic modeling approach to help researchers and scientists gain a deeper understanding of the large and heterogeneous networks formed by authors, papers, conferences, journals and organizations. The system is subsequently able to extract researchers’ profiles automatically from the Web and integrates them with published papers by a way of a process that first performs name disambiguation. Then a generative probabilistic model is devised to simultaneously model the different entities while providing a topic-level expertise search. In addition, AMiner offers a set of researcher-centered functions, including social influence analysis, relationship mining, collaboration recommendation, similarity analysis, and community evolution. The system has been in operation since 2006 and has been accessed from more than 8 million independent IP addresses residing in more than 200 countries and regions.

Relationship prediction in knowledge graph aims to identify and infer new relationships from existing data, and provides knowledge services for many downstream tasks. At present, many researches solve the link prediction problem between entities by mapping entities and relations into a vector space or searching the paths between entities. These methods only consider the influence of single path or first-order information but ignore more complex relation information between entities. Therefore, this paper proposes a novel link prediction method based on subgraph reasoning in knowledge graph, uses the subgraph structure to obtain the entity pair neighborhood structure information, combines the advantages of representation learning and path reasoning, and realizes the relationship prediction between entities. This paper first extends the paths between entities to subgraphs, constructs node subgraph and relationship subgraph from entity level and relationship level respectively, then combines the graph embedding representation with the graph neural network to calculate the subgraph features, to get richer entity characteristics and relationship characteristics. Finally, this paper calculates the neighborhood structure information of entity pairs from the subgraph structure to conduct link prediction between entities. Experimental results demons-trate that the proposed approach outperforms other reasoning-based link prediction methods on two benchmark datasets.

Traffic accident forecasting is very important for urban public security, emergency treatment and construc-tion planning. However, the following problems still exist when forecasting traffic accident risk. Firstly, traffic accidents are affected by multiple factors, such as weather and road conditions. Besides, there are multi-scale correlations in the spatial dimension, i.e. local region spatial-temporal correlation and global region spatial-temporal similarity. Meanwhile, there is zero-inflated issue in the forecasting because of few traffic accidents in reality. Therefore, it is very challenging to forecast traffic accidents accurately, and existing traffic accident forecasting methods cannot take all the above problems into account. A novel model, named local and global spatial-temporal networks (ST-RiskNet), for traffic accident risk forecasting is proposed. The ST-RiskNet takes multi-source factors that affect traffic accidents into consideration, such as time, weather, traffic flow. It uses a local region spatial-temporal correlation module and a global region spatial-temporal similarity module to model the multi-scale spatial-temporal correlation and similarity simultaneously. Meanwhile, a sample weighted loss function is designed to solve the zero-inflated problem, which pays more weights to the higher risk samples. Extensive experiments on two real-world traffic accident datasets demonstrate the effectiveness of the ST-RiskNet against the state-of-the-art baseline methods.

With the increasing accumulation of spatial-temporal trajectory data, location-based data mining has recently been extensively studied. A fundamental research topic in this field is learning the embedding vectors of locations through self-supervised pre-training. Pre-trained embedding vectors can utilize the highly available unlabeled trajectory data, and benefit downstream tasks in multiple aspects. However, most existing methods ignore the temporal information hidden in the visited time of locations in trajectories. Considering that human activities are highly regulated by specific periods of a day, temporal information can reflect some intrinsic characteristics of locations, so it is necessary to fuse them into location embedding vectors. In this paper, we propose a Time-Aware Location Embedding (TALE) pre-training method based on the CBOW framework, which is able to incorporate temporal information into the learned embedding vectors of locations. A novel temporal tree structure is designed to extract temporal information during the calculation of Hierarchical Softmax. In order to verify the effectiveness of TALE, we apply the learned embedding vectors into three downstream location-based prediction tasks, i.e., location classification, location visitor flow prediction and user next location prediction. Experiments are conducted on four real-world user trajectory datasets, and the experimental results demonstrate that our TALE model can obviously help downstream tasks gain better performance. [ABSTRACT FROM AUTHOR]

Accurate traffic forecasting is critical in improving safety, stability, and efficiency of intelligent transportation systems. Despite years of studies, accurate traffic prediction still faces the following challenges, including modeling the dynamics of traffic data along both temporal and spatial dimensions, and capturing the periodicity and the spatial heterogeneity of traffic data, and the problem is more difficult for long-term forecast. In this paper, we propose an Attention based Spatial-Temporal Graph Neural Network (ASTGNN) for traffic forecasting. Specifically, in the temporal dimension, we design a novel self-attention mechanism that is capable of utilizing the local context, which is specialized for numerical sequence representation transformation. It enables our prediction model to capture the temporal dynamics of traffic data and to enjoy global receptive fields that is beneficial for long-term forecast. In the spatial dimension, we develop a dynamic graph convolution module, employing self-attention to capture the spatial correlations in a dynamic manner. Furthermore, we explicitly model the periodicity and capture the spatial heterogeneity through embedding modules. Experiments on five real-world traffic flow datasets demonstrate that ASTGNN outperforms the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Forecasting traffic inflows and outflows is crucial for intelligent transportation applications such as traffic management and risk assessment. Recently, deep learning models, which focus on capturing spatio-temporal correlations between stations (locations) by constructing Spatio-Temporal Feature Learners (STFL), have achieved promising performance in traffic inflows and outflows prediction. However, two unresolved issues limit the performance of these models. i) dynamic and heterogeneous intra- and inter-relationships between flows are ignored, and ii) the STFL in these models cannot capture the global information. To address the above issues, we propose a novel deep Spatio-Temporal Network framework based on Multi-Relational learning (MR-STN) for predicting traffic inflows and outflows. Specifically, a multi-relational learning module is designed to comprehensively model three kinds of relationships between flows while extracting diverse spatio-temporal features. In this module, an enhanced STFL is developed to capture both local and global information. Then, a feature fusion module is introduced to extract fused features for inflows and outflows respectively via a gated fusion mechanism. On this basis, the prediction module uses fusion features to generate future inflows and outflows. Finally, we implement the proposed framework with four state-of-the-art graph-based deep spatio-temporal models to demonstrate its generality and superiority. Extensive experiments on three datasets show that the proposed framework can significantly boost the performance of existing models. [ABSTRACT FROM AUTHOR]

Reliable traffic prediction is critical to improve safety, stability, and efficiency of intelligent transportation systems. However, traffic prediction is a very challenging problem because traffic data are a typical type of spatio-temporal data, which simultaneously shows correlation and heterogeneity both in space and time. Most existing works can only capture the partial properties of traffic data and even assume that the effect of correlation on traffic prediction is globally invariable, resulting in inadequate modeling and unsatisfactory prediction performance. In this paper, we propose a novel end-to-end deep learning model, called ST-3DNet, for traffic raster data prediction. ST-3DNet introduces 3D convolutions to automatically capture the correlations of traffic data in both spatial and temporal dimensions. A novel recalibration (Rc) block is proposed to explicitly quantify the difference of the contributions of the correlations in space. Considering two kinds of temporal properties of traffic data, i.e., local patterns and long-term patterns, ST-3DNet employs two components consisting of 3D convolutions and Rc blocks to, respectively, model the two kinds of patterns and then aggregates them together in a weighted way for the final prediction. The experiments on several real-world traffic datasets, viz., traffic congestion data and crowd flows data, demonstrate that our ST-3DNet outperforms the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Objective: Traditional Chinese medicine (TCM) is a unique and complex medical system that has developed over thousands of years. This article studies the problem of automatically extracting meaningful relations of entities from TCM literature, for the purposes of assisting clinical treatment or poly-pharmacology research and promoting the understanding of TCM in Western countries.Methods: Instead of separately extracting each relation from a single sentence or document, we propose to collectively and globally extract multiple types of relations (eg, herb-syndrome, herb-disease, formula-syndrome, formula-disease, and syndrome-disease relations) from the entire corpus of TCM literature, from the perspective of network mining. In our analysis, we first constructed heterogeneous entity networks from the TCM literature, in which each edge is a candidate relation, then used a heterogeneous factor graph model (HFGM) to simultaneously infer the existence of all the edges. We also employed a semi-supervised learning algorithm estimate the model's parameters.Results: We performed our method to extract relations from a large dataset consisting of more than 100,000 TCM article abstracts. Our results show that the performance of the HFGM at extracting all types of relations from TCM literature was significantly better than a traditional support vector machine (SVM) classifier (increasing the average precision by 11.09%, the recall by 13.83%, and the F1-measure by 12.47% for different types of relations, compared with a traditional SVM classifier).Conclusion: This study exploits the power of collective inference and proposes an HFGM based on heterogeneous entity networks, which significantly improved our ability to extract relations from TCM literature. [ABSTRACT FROM AUTHOR]

People usually travel to the same destinations and for the same purposes together with other people in groups. Inferring the travel purposes of passenger groups is a very interesting research problem in the field of passenger transport, because it can help us to better understand passengers and should bring about meaningful changes for personalized travel service and decision making of passenger carriers, organizations, and even governments. In this paper, we attempt to solve this problem by utilizing the historical travel records of passengers. To overcome the constraint of the independent and identical distribution assumption of traditional classifiers, we propose a novel iterative classification approach based on the idea of collective inference. First, we construct cotravel networks by extracting social relations between passengers from their historical travel records that are available in carriers' passenger information systems. Then, we generate a series of sophisticated features for each passenger group in the context of cotravel networks to capture the link structure information between passengers and use the overlapping relations between passenger groups to capture the probabilistic dependence relations between their labels. Finally, we collectively infer the labels of all the groups in an iterative way. Experimental results on a real data set of passenger travel records in the field of civil aviation demonstrate that our proposed iterative classification approach can efficiently infer the travel purposes of passenger groups. [ABSTRACT FROM PUBLISHER]