Showing items 1 - 8 of 8

Your selections:

A shared bus profiling scheme for smart cities based on heterogeneous mobile crowdsourced data

- Kong, Xiangjie, Xia, Feng, Li, Jianxin, Hou, Mingliang, Li, Menglin, Xiang, Yong

  • Authors: Kong, Xiangjie , Xia, Feng , Li, Jianxin , Hou, Mingliang , Li, Menglin , Xiang, Yong
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Informatics Vol. 16, no. 2 (2020), p. 1436-1444
  • Full Text:
  • Reviewed:
  • Description: Mobile crowdsourcing (MCS), as an effective and crucial technique of Industrial Internet of Things, is enabling smart city initiatives in the real world. It aims at incorporating the intelligence of dynamic crowds to collect and compute decentralized ubiquitous sensing data that can be used to solve major urbanization problems such as traffic congestion. The shared bus, as a neotype transportation mode, aims at improving the resource utilization rate and maintaining the advantages of convenience and economy. In this article, we provide a scheme to profile shared buses through heterogeneous mobile crowdsourced data (TRProfiling). First, we design an MCS-based shared bus data generation and collection solution to overcome the aforementioned data scarcity issue. Then, we propose a travel profiling to profile resident travel and design a method called multiconstraint evolution algorithm to optimize the routes. Experimental results demonstrate that TRProfiling has an excellent performance in satisfying passengers' travel requirements. © 2005-2012 IEEE.

MODEL : motif-based deep feature learning for link prediction

- Wang, Lei, Ren, Jing, Xu, Bo, Li, Jianxin, Luo, Wei, Xia, Feng

  • Authors: Wang, Lei , Ren, Jing , Xu, Bo , Li, Jianxin , Luo, Wei , Xia, Feng
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Computational Social Systems Vol. 7, no. 2 (2020), p. 503-516
  • Full Text:
  • Reviewed:
  • Description: Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this article, we propose a novel embedding algorithm that incorporates network motifs to capture higher order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms (by 20%) and the state-of-the-art embedding-based algorithms (by 19%). © 2014 IEEE.

Web of students : class-level friendship network discovery from educational big data

- Guo, Teng, Tang, Tang, Zhang, Dongyu, Li, Jianxin, Xia, Feng

  • Authors: Guo, Teng , Tang, Tang , Zhang, Dongyu , Li, Jianxin , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 22nd International Conference on Web Information Systems Engineering, WISE 2021 p. 497-511
  • Full Text: false
  • Reviewed:
  • Description: Classmate friendships are a major aspect of university social experience. Taking classes together is one of the main ways for students to build friendships. Consequently, class-level friendship networks have attracted tremendous attention from researchers. They are also very useful in student support and early intervention. However, these networks are normally invisible for educators. Discovering such an important web of students effectively is a pressing problem. Against this background, we propose a data-driven framework called CANDY which automatically discovers the class-level friendship networks based on educational big data. We first represent features through representation learning methods. Secondly, the data is augmented with the randomly shuffling method. Thirdly, a conditional generative adversarial network model is used to mine the class-level friendship networks. A deep adversarial optimization strategy is proposed here for problems caused by network sparsity. To evaluate the performance of the proposed approach, we build a real-world dataset that contains rich student information. Extensive experiments have been conducted and the results demonstrate the effectiveness of our framework. © 2021, Springer Nature Switzerland AG.

Predictive representation learning in motif-based graph networks

- Zhang, Kaiyuan, Yu, Shuo, Wan, Liangtian, Li, Jianxin, Xia, Feng

  • Authors: Zhang, Kaiyuan , Yu, Shuo , Wan, Liangtian , Li, Jianxin , Xia, Feng
  • Date: 2019
  • Type: Text , Book chapter
  • Relation: AI 2019: Advances in Artificial Intelligence Chapter 15 p. 177-188
  • Full Text: false
  • Reviewed:
  • Description: Link prediction is an important task for analyzing social networks which also has other applications such as bioinformatics and e-commerce. Network representation learning (NRL), which can significantly enhance the performance for link prediction, has attracted much attention in recent years. However, the existing NRL methods mainly focus on observed network structures without considering hidden prediction knowledge in the representation space. Meanwhile, some random walk based NRL methods are dissatisfactory to learn link knowledge in dense networks with large scales. In this paper, we propose a predictive representation learning (PRL) model, which unifies node representations and motif-based structures, to improve prediction ability of NRL. We firstly enhance node representations based on motif-biased random walks and then employ L2-SVM to learn motif-connected node-pairs. By jointly optimizing two objectives of existent and nonexistent edges representations, we preserve more information of nodes in representation space based on supervised learning. To evaluate the performance of our proposed model, we implement experiments on 5 real data sets. Simulation results illustrate that our proposed model achieves better link prediction performance compared with other state-of-the-arts methods.

Community-diversified influence maximization in social networks

- Li, Jianxin, Cai, Taotao, Deng, Ke, Wang, Xinjue, Sellis, Timos, Xia, Feng

  • Authors: Li, Jianxin , Cai, Taotao , Deng, Ke , Wang, Xinjue , Sellis, Timos , Xia, Feng
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Information Systems Vol. 92, no. (2020), p.
  • Full Text:
  • Reviewed:
  • Description: To meet the requirement of social influence analytics in various applications, the problem of influence maximization has been studied in recent years. The aim is to find a limited number of nodes (i.e., users) which can activate (i.e. influence) the maximum number of nodes in social networks. However, the community diversity of influenced users is largely ignored even though it has unique value in practice. For example, the higher community diversity reduces the risk of marketing campaigns as you should not put all your eggs in one basket; the diversity can also prolong the effect of a marketing campaign in the future promotion. Motivated by this observation, this paper investigates Community-diversified Influence Maximization (CDIM) problem to efficiently find k nodes such that, if a message is initiated and spread by the k nodes, the number as well as the community diversity of the activated nodes will be maximized at the end of propagation process. This work proposes a metric to measure the community-diversified influence and addresses a series of computational challenges. Two algorithms and an innovative CPSP-Tree index have been developed. This study also investigates the situation that community definition is not specified. The effectiveness and efficiency of the proposed solutions have been verified through extensive experimental studies on five real-world social network datasets. © 2020 Elsevier Ltd

CHIEF : clustering With higher-order motifs in big networks

- Xia, Feng, Yu, Shuo, Liu, Chengfei, Li, Jianxin, Lee, Ivan

  • Authors: Xia, Feng , Yu, Shuo , Liu, Chengfei , Li, Jianxin , Lee, Ivan
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Network Science and Engineering Vol. 9, no. 3 (2022), p. 990-1005
  • Full Text:
  • Reviewed:
  • Description: Clustering network vertices is an enabler of various applications such as social computing and Internet of Things. However, challenges arise for clustering when networks increase in scale. This paper proposes CHIEF (Clustering with HIgher-ordEr motiFs), a solution which consists of two motif clustering techniques: standard acceleration CHIEF-ST and approximate acceleration CHIEF-AP. Both algorithms firstly find the maximal $k$-edge-connected subgraphs within the target networks to lower the network scale by optimizing the network structure with maximal $k$-edge-connected subgraphs, and then use heterogeneous four-node motifs clustering in higher-order dense networks. For CHIEF-ST, we illustrate that all target motifs will be kept after this procedure when the minimum node degree of the target motif is equal or greater than $k$. For CHIEF-AP, we prove that the eigenvalues of the adjacency matrix and the Laplacian matrix are relatively stable after this step. CHIEF offers an improved efficiency of motif clustering for big networks, and it verifies higher-order motif significance. Experiments on real and synthetic networks demonstrate that the proposed solutions outperform baseline approaches in large network analysis, and higher-order motifs outperform traditional triangle motifs in clustering. © 2022 IEEE Computer Society. All rights reserved.

Exploring human mobility for multi-pattern passenger prediction : a graph learning framework

- Kong, Xiangjiea, Wang, Kailai, Hou, Mingliang, Xia, Feng, Karmakar, Gour, Li, Jianxin

  • Authors: Kong, Xiangjiea , Wang, Kailai , Hou, Mingliang , Xia, Feng , Karmakar, Gour , Li, Jianxin
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 23, no. 9 (2022), p. 16148-16160
  • Full Text:
  • Reviewed:
  • Description: Traffic flow prediction is an integral part of an intelligent transportation system and thus fundamental for various traffic-related applications. Buses are an indispensable way of moving for urban residents with fixed routes and schedules, which leads to latent travel regularity. However, human mobility patterns, specifically the complex relationships between bus passengers, are deeply hidden in this fixed mobility mode. Although many models exist to predict traffic flow, human mobility patterns have not been well explored in this regard. To address this research gap and learn human mobility knowledge from this fixed travel behaviors, we propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN). Firstly, we construct a novel sharing-stop network to model relationships between passengers based on bus record data. Then, we employ GCN to extract features from the graph by learning useful topology information and introduce a deep clustering method to recognize mobility patterns hidden in bus passengers. Furthermore, to fully utilize spatio-temporal information, we propose GCN2Flow to predict passenger flow based on various mobility patterns. To the best of our knowledge, this paper is the first work to adopt a multi-pattern approach to predict the bus passenger flow by taking advantage of graph learning. We design a case study for optimizing routes. Extensive experiments upon a real-world bus dataset demonstrate that MPGCN has potential efficacy in passenger flow prediction and route optimization. © 2000-2011 IEEE.

iTopic: influential topic discovery from information networks via keyword query

- Li, Jianxin, Liu, Chengfei, Chen, Lu, He, Zhenying, Datta, Amitava, Xia, Feng

  • Authors: Li, Jianxin , Liu, Chengfei , Chen, Lu , He, Zhenying , Datta, Amitava , Xia, Feng
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: WWW '17: 26th International World Wide Web Conference Perth Australia; April 3 - 7, 2017 p. 231-235
  • Full Text: false
  • Reviewed:
  • Description: The rapid growth of information networks provides a significant opportunity for people to learn the world and find useful information for decision making. To find influential topics in a given context, instead of searching widely over the whole information network, normally it is wise to find the related communities first and then identify the influential topics in those communities. In this demonstration, we present a novel framework to compute the correlated sub-networks from a large information network such as CiteSeerX based on a user’s keyword query, and to extract the influential topics from each correlated network. To help users understand the influential topics as a whole, we utilize a word cloud to represent the discovered topics for each correlated network. As such, multiple word clouds can be generated for different correlated networks, by which users can easily pick up their interested ones by reading the visualized topic descriptions over word clouds. To determine the sizes of different terms in a word cloud, we introduce a scoring scheme for assessing the influence of these terms in the corresponding networks. We demonstrate the functionality of our influential topic system, called iTopic, using the CiteSeerX information network data.
  • 1