Networks are known as perfect tools for modeling various types of systems. In the literature of network mining, frequent subgraph mining is considered as the essence of mining network data. In this problem, the dataset is composed of networks represent…
Large scale machine learning (ML) and deep learning (DL) platforms face challenges when integrated with deduplication enabled storage clusters. In the quest to achieve smart and efficient storage utilization, removal of duplicate data introduces bottle…
Outsourcing data to the cloud has become popular due to the big data challenges. However, security concerns compel the outsourced data to be encrypted before sending them to the cloud, which lowers their utility and efficiency. The range query plays a …
In the era of big data, standard analysis tools may be inadequate for making inference and there is a growing need for more efficient and innovative ways to collect, process, analyze and interpret the massive and complex data. We provide an overview of…
Driving behaviour understanding is of vital importance in improving transportation safety and promoting the development of Intelligent Transportation Systems (ITS). As a long-standing research topic in driving behaviour analysis, driving style identifi…
Dimensionality reduction is commonly used for identifying and analyzing patterns in the visual analysis of multi-dimensional datasets. The selection of subspaces is a core building block in projecting high-dimensional data to low-dimensional space, whi…
A Stochastic Gradient Descent (SGD)-based Latent Factor Analysis (LFA) model is highly efficient in representative learning on a High-Dimensional and Sparse (HiDS) matrix, where the learning rate adaptation is vital in its efficiency and practicability…
Temporal subgraph mining is recently ubiquitous. Identifying diversified and lasting ingredients is a fundamental problem in analyzing temporal networks. In this paper, we investigate the problem of finding diversified lasting cohesive subgraphs from temporal networks. Specifically, we first introduce a new model, called maximal lasting $(k,sigma)$(k,σ)-core, for characterizing lasting cohesive subgraphs on temporal networks so as to the nodes in the subgraph are connected densely and also the subgraph’s structure remains unchanged for a period of time. To enhance the diversity of results, we then formulate a diversified lasting cohesive subgraphs problem, which finds $r$r maximal lasting $(k,sigma)$(k,σ)-cores with maximum coverage regarding the number of vertices and timestamps. Unfortunately, we show that the optimization problem is NP-hard. To tackle this issue, we first devise a greedy algorithm named GreLC with (1-1/e) approximation ratio. However, GreLC has prohibitively high time and space complexity, resulting in poor scalability. Then, an improved DFS-based search algorithm called TopLC with 1/4 approximation rati-
is proposed to lower the computational cost. Finally, empirical studies on six real-world temporal networks demonstrate that the proposed solutions perform efficiently and accurately, and our model is better than temporal cohesive subgraphs detected by existing approaches.
With the thriving of the Android ecosystem, codes are widely reused in Android apps in the form of third-party libraries. Recent research shows that emerging third-party libraries may introduce a lot of privacy risks and other security threats. Neverth…
In complex event processing (CEP), load shedding is performed to maintain a given latency bound during overload situations when there is a limitation on resources. However, shedding load implies degradation in the quality of results (QoR). Therefore, i…