期刊： DATA MINING AND KNOWLEDGE DISCOVERY, 2021; 35 (3)
In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilit......
期刊： DATA MINING AND KNOWLEDGE DISCOVERY, 2021; 35 (5)
In this paper, a new alternating nonmonotone projected Barzilai-Borwein (BB) algorithm is developed for solving large scale problems of nonnegative ma......
期刊： DATA MINING AND KNOWLEDGE DISCOVERY, 2021; 35 (4)
Bug localization utilizes the collected bug reports to locate the buggy source files. The state of the art falls short in handling the following three......
期刊： DATA MINING AND KNOWLEDGE DISCOVERY, 2021; 35 (5)
Network embedding, also known as graph embedding and network representation learning, is an effective method for representing graphs or network data i......
期刊： DATA MINING AND KNOWLEDGE DISCOVERY, ; ()
Multi-label learning deals with data examples which are associated with multiple class labels simultaneously. Despite the success of existing approach......
期刊： DATA MINING AND KNOWLEDGE DISCOVERY, 2021; 35 (2)
In modern social media platforms, an effective content recommendation should benefit both creators to bring genuine benefits to them and consumers to ......
期刊： DATA MINING AND KNOWLEDGE DISCOVERY, 2020; 34 (3)
Community detection is one of the most important issues in modern network science. Although numerous community detection algorithms have been proposed......
期刊： DATA MINING AND KNOWLEDGE DISCOVERY, 2020; 34 (6)
Product reviews are extremely valuable for online shoppers in providing purchase decisions. Driven by immense profit incentives, fraudsters deliberate......
期刊： DATA MINING AND KNOWLEDGE DISCOVERY, 2020; 34 (5)
Recent studies have shown that graph convolution networks (GCNs) are vulnerable to carefully designed attacks, which aim to cause misclassification of......
期刊： DATA MINING AND KNOWLEDGE DISCOVERY, 2020; 34 (6)
Structural network alignment utilizes the topological structure information to find correspondences between nodes of two networks. Researchers have pr......
期刊： DATA MINING AND KNOWLEDGE DISCOVERY, 2020; 34 (6)
Due to the ubiquitous presence of missing values (MVs) in real-world datasets, the MV imputation problem, aiming to recover MVs, is an important and f......
期刊： DATA MINING AND KNOWLEDGE DISCOVERY, 2020; 34 (1)
Knowledge graphs have shown increasing importance in broad applications such as question answering, web search, and recommendation systems. The object......
期刊： DATA MINING AND KNOWLEDGE DISCOVERY, 2019; 33 (1)
The cloud based security service generates a new type of security data, which indicates the occurrence of executable files in end hosts. With the basi......
期刊： DATA MINING AND KNOWLEDGE DISCOVERY, 2019; 33 (1)
Random walk-based sampling methods are gaining popularity and importance in characterizing large networks. While powerful, they suffer from the slow m......
期刊： DATA MINING AND KNOWLEDGE DISCOVERY, 2019; 33 (2)
We revisit in this paper the problem of inferring a diffusion network from information cascades. In our study, we make no assumptions on the underlyin......
