Community detection is a fundamental yet important task for characterizing and understanding the structure of attributed graphs. Existing methods mainly focus on the structural tightness and attribute similarity among nodes in a community. However, ...

In this paper, we develop an approach to exploit multiple disparate wireless communication technologies simultaneously to enhance covertness of a communication link. Specifically, given two available communication modalities between a pair of friendly ...

Recent years have seen rapid progress in network representation learning, which removes the need for burdensome feature engineering and facilitates downstream network-based tasks. In reality, networks often exhibit heterogeneity, which means there may ...

Individual behavioral engagement is an important indicator of active learning in collaborative settings, encompassing multidimensional behaviors mediated through various interaction modes. Little existing work has explored the use of multimodal process ...

In recent years, multi-behavior information has been utilized to address data sparsity and cold-start issues. The general multi-behavior models capture multiple behaviors of users to make the representation of relevant features more fine-grained and ...

User interfaces (UI) of desktop, web, and mobile applications involve a hierarchy of objects (e.g., applications, screens, view class, and other types of design objects) with multimodal (e.g., textual and visual) and positional (e.g., spatial location, ...

Link prediction between two nodes is a critical task in graph machine learning. Most approaches are based on variants of graph neural networks (GNNs) that focus on transductive link prediction and have high inference latency. However, many real-world ...

Exact string matching in labeled graphs is the problem of searching paths of a graph G=(V, E) such that the concatenation of their node labels is equal to a given pattern string P[1.m]. This basic problem can be found at the heart of more complex ...

Data explosion in the information society drives people to develop more effective ways to extract meaningful information. Extracting semantic information and relational information has emerged as a key mining primitive in a wide variety of practical ...

Access selection is an important key in heterogeneous networks, and the design of a new algorithm for decision is not a trivial task. Different aspects must be taken into consideration while designing a new decision algorithm, including both users' ...

The massive growth in the current and envisaged cellular traffic lead to innovations in 5G heterogeneous networks (HetNets) and implementation technologies. The small cells (SCs) or small base stations (SBS) aided macro base station (MBS) topology in ...

As hospitals move towards automating and integrating their computing systems, more fine-grained hospital operations data are becoming available. These data include hospital architectural drawings, logs of interactions between patients and healthcare ...

User interfaces (UI) of desktop, web, and mobile applications involve a hierarchy of objects (e.g. applications, screens, view class, and other types of design objects) with multimodal (e.g. textual, visual) and positional (e.g. spatial location, ...

The paper proposes a security management method that provides risk analysis and processing based on the use of an intelligent agent system distributed over network segments, which provides a forecast based on a modular hybrid time series forecasting ...

With the explosive growth of new graduates with research degrees every year, unprecedented challenges arise for early-career researchers to find a job at a suitable institution. This study aims to understand the behavior of academic job transition and ...

While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of applications, recent studies exposed important shortcomings in their ability to capture heterogeneous structures and attributes of an underlying graph. Furthermore, ...

We consider the problem of learning efficient and inductive graph convolutional networks for text classification with a large number of examples and features. Existing state-of-the-art graph embedding based methods such as predictive text embedding (PTE)...

In this article, we introduce a generalization of graphlets to heterogeneous networks called typed graphlets. Informally, typed graphlets are small typed induced subgraphs. Typed graphlets generalize graphlets to rich heterogeneous networks as they ...

Mobile users are interested in utilising high network capabilities without time and place constraints. However, with a high level of interest in the usage of mobile phones and internet facilities, the limited capacity of terrestrial base stations (BSs) is ...

This study presents the performance evaluation of a data‐offloading algorithm developed and implemented in the framework of a software‐defined network (SDN). The algorithm senses the network congestion and takes into account the constraints imposed to the ...