Dynamic link prediction aims to predict future connections among unconnected nodes in a network. It can be applied for friend recommendations, link completion, and other tasks. Network representation learning algorithms have demonstrated considerable effectiveness in various prediction tasks. However, most network representation learning algorithms are based on homogeneous networks and static networks for link prediction that do not consider rich semantic and dynamic information. Additionally, existing dynamic network representation learning methods neglect the neighborhood interaction structure of the node. In this work, we design a neighbor-enhanced dynamic heterogeneous attributed network embedding method (NeiDyHNE) for link prediction. In light of the impressive achievements of the heuristic methods, we learn the information of common neighbors and neighbors’ interaction in heterogeneous networks to preserve the neighbors proximity and common neighbors proximity. NeiDyHNE encodes the attributes and neighborhood structure of nodes as well as the evolutionary features of the dynamic network. More specifically, NeiDyHNE consists of the hierarchical structure attention module and the convolutional temporal attention module. The hierarchical structure attention module captures the rich features and semantic structure of nodes. The convolutional temporal attention module captures the evolutionary features of the network over time in dynamic heterogeneous networks. We evaluate our method and various baseline methods on the dynamic link prediction task. Experimental results demonstrate that our method is superior to baseline methods in terms of accuracy.

Oracle Bone Inscriptions (OBI) is one of the oldest scripts in the world. The rejoining of Oracle Bone (OB) fragments is of vital importance to the research of ancient scripts and history. Although significant progress has been achieved in the past decades, the rejoining work still heavily relies on domain knowledge and manual work, thus remains a low efficient and time-consuming process Therefore, an automatic and practical algorithm/system for OB rejoining is of great value to the OBI community. To this end, we collect a real-world dataset for rejoining Oracle Bone fragments, namely OB-Rejoin, which consists of 998 OB rubbing images that suffer from low quality image problems, due to intrinsic underground eroding over time and extrinsic imaging conditions in the past. Moreover, a practical Self-Supervised Splicing Network, S3-Net, is proposed to rejoin the OB fragments based on shape similarity of their borderlines. Specifically, we first transform the manually annotated borderline strokes of OB images into times series style shape representations, which are fed as input to a Generative Adversarial Network for augmenting positive pairs of rejoinable OBs for each OB fragment that does not have rejoinable counterparts. A Siamese network is trained on such augmented data in a contrastive learning manner to retrieve the matching OB fragments of an unseen query from an OB fragment gallery. Experiments on the OB-Rejoin benchmark show that our data-driven approach outperforms two recent methods for time-series analysis. In order to demonstrate its practical potential, we deploy the proposed S3-Net method in real tests and ultimately discover dozens of new rejoinings missed by domain experts for decades.

We study the efficient, generic processing of moving K nearest neighbor (MKNN) and top-K spatial keyword (MKSK) queries. Such generic processing is attractive during high query loads. We propose GridVoronoi--an index that enables users to find the spatial nearest neighbor (NN) from uniformly distributed datasets in almost O(1) time. GridVoronoi is based upon Voronoi diagram which has proven to be highly efficient in exploring the local neighborhood of a given Voronoi cell. However, Voronoi diagram needs a method to promptly find out which Voronoi cell contains the query point. So we add a virtual (i.e., conceptual) grid to the Voronoi diagram. For any query point, GridVoronoi first uses the grid to compute which Voronoi cell contains the query, next utilizes Voronoi diagram to quickly find the NN and KNN (i.e., K nearest neighbors) of the query.

Usage data mining is an important research area with applications in various fields. However, usage data is usually considered streaming, due to its high volumes and rates. Because of these characteristics, we only have access, at any point in time, to a small fraction of the stream. When the data is observed through such a limited window, it is challenging to give a reliable description of the recent usage data. We study the important consequences of these constraints, through the “bounce rate” problem and the clustering of usage data streams. Then, we propose the ABS (Anti-Bouncing Stream) model which combines the advantages of previous models but discards their drawbacks. First, under the same resource constraints as existing models in the literature, ABS can better model the recent data. Second, owing to its simple but effective management approach, the data in ABS is available at any time for analysis. We demonstrate its superiority through a theoretical study and experiments on two real-world data sets.