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In recent years, online social networks (OSNs) have thoroughly penetrated people’s lives. Since information always flows along with various online relations in OSNs, analysing these relations becomes one of the most fundamental problems in various ...

Conditional p-center problem is one of the classical facility location problems, which aims to find p facilities meeting the given distance condition with q pre-existing facilities. It is worth noting that, with the proliferation of the social ...

Every epidemic affects the real lives of many people around the world and leads to terrible consequences. Recently, many tweets about the COVID-19 pandemic have been shared publicly on social media platforms. The analysis of these tweets is ...

Social Group Recommendation (SGR) is a critical task to recommend items to a group of users in social network platforms, such as Meetup, Douban, Mofengwo, etc. Recently, many state-of-the-art works have addressed the group decision making with pre-...

Personalized tag recommender systems automatically recommend users a set of tags used to annotate items according to users’ past tagging information. Learning the representations of involved entities (i.e. users, items and tags) and capturing the ...

Traditional congestion control protocols may fail to achieve consistently-high performance over a wide range of networking environments as their hardwired policies are optimized over specific network conditions. In this paper, we depart from ...$$$$

Efficient and timely dispatch of maintenance personnel for fault detection and failure recovery play a key role towards safe operation of power grid and has become a challenging issue. To address this challenge, this paper proposes a new optimal ...

Distributed Denial of Service (DDoS) attacks are among the most severe threats in cyberspace. The existing methods are only designed to decide whether certain types of DDoS attacks are ongoing. As a result, they cannot detect other types of ...

With the rapid development of the Internet and the widespread usage of mobile terminals, data-driven user profiling has become possible. User profiles describe the user’s overall behavior characteristic from multiple perspectives (e.g. basic ...

In recent years, artificial intelligence technologies have been successfully applied in time series prediction and analytic tasks. At the same time, a lot of attention has been paid to financial time series prediction, which targets the ...

Session-based recommendation aims to predict the next item to be interacted by a specific type of behavior (e.g., click or purchase) within a session. However, the main challenge comes from the lack of interactions in the target behavior. Despite ...

Medical reports have significant clinical value to radiologists and specialists, especially during a pandemic like COVID. However, beyond the common difficulties faced in the natural image captioning, medical report generation specifically ...

We present a novel online decision-making solution, where the optimal path of a given decision tree is dynamically found based on the contextual bandits analysis. At each round, the learner finds a path in the decision tree by making a sequence of ...

Representation learning techniques for knowledge graphs (KGs) are crucial for constructing knowledge-driven decisions in complex network data application scenarios. Most existing methods focus mainly on structured information, ignoring the ...

Effective knowledge graph storage management is identified as the basic premise to make full use of knowledge graphs. Due to the lack of performance evaluation for knowledge graph stores, it is difficult for users to decide which one is the best. ...

The query processing on knowledge graphs has attracted significant attention in the past years. Different from the traditional query processing on knowledge graphs, the example query method can capture the users’ query intentions by providing ...

The goal of multi-view spectral clustering (MVSC) is to explore the intrinsic cluster structures embedded in the multi-view data and group the learned optimal feature embeddings into different clusters based on similarity measurement. Although ...

Distant supervised relation extraction which is to extract heterogeneous relations from text data without manual annotation has been widely used in decision-making tasks such as question answering or recommendation system. However, existing ...

Graphs have been widely adopted in various fields, where many graph models are developed. Most of previous research focuses on unipartite or homogeneous graph analysis. In this graphs, the relationships between the same type of entities are ...

Clustering is a basic task of data analysis and decision making. Recently, graph convolution network (GCN) based deep clustering frameworks have produced the state-of-the-art performance. However, the traditional GCN has not fully learnt the ...

Rule mining is an important and challenging task in data mining. Although many state-of-art algorithms have been proposed on dense data, they are not effectively adaptive for sparse data, such as sparse heterogeneous networks. Transfer learning ...

Personalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent clients, e.g. smartphones or enterprises. FL can mitigate the data ...

Recently, researches on dynamic decision-making based on streaming data are in full swing. As an indispensable technology for data management and analysis, indexing methods are also evolving. The indexing paradigm named learned index has been ...$\mathrm{}$