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HoloScope: Topology-and-Spike Aware Fraud Detection

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arXiv 2017年

作者： Liu, Shenghua Hooi, Bryan Faloutsos, Christos CAS Key Laboratory of Network Data Science & Technology Institute of Computing Technology Chinese Academy of Sciences Carnegie Mellon University

As online fraudsters invest more resources, including purchasing large pools of fake user accounts and dedicated IPs, fraudulent attacks become less obvious and their detection becomes increasingly challenging. Existing approaches such as average degree maximization suffer from the bias of including more nodes than necessary, resulting in lower accuracy and increased need for manual verification. Hence, we propose HoloScope, which uses information from graph topology and temporal spikes to more accurately detect groups of fraudulent users. In terms of graph topology, we introduce "contrast suspiciousness," a dynamic weighting approach, which allows us to more accurately detect fraudulent blocks, particularly low-density blocks. In terms of temporal spikes, HoloScope takes into account the sudden bursts and drops of fraudsters’ attacking patterns. In addition, we provide theoretical bounds for how much this increases the time cost needed for fraudsters to conduct adversarial attacks. Additionally, from the perspective of ratings, HoloScope incorporates the deviation of rating scores in order to catch fraudsters more accurately. Moreover, HoloScope has a concise framework and sub-quadratic time complexity, making the algorithm reproducible and scalable. Extensive experiments showed that HoloScope achieved significant accuracy improvements on synthetic and real data, compared with state-of-the-art fraud detection methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Drops

Marked temporal dynamics modeling based on recurrent neural network

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arXiv 2017年

作者： Wang, Yongqing Liu, Shenghua Shen, Huawei Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

We are now witnessing the increasing availability of event stream data, i.e., a sequence of events with each event typically being denoted by the time it occurs and its mark information (e.g., event type). A fundamental problem is to model and predict such kind of marked temporal dynamics, i.e., when the next event will take place and what its mark will be. Existing methods either predict only the mark or the time of the next event, or predict both of them, yet separately. Indeed, in marked temporal dynamics, the time and the mark of the next event are highly dependent on each other, requiring a method that could simultaneously predict both of them. To tackle this problem, in this paper, we propose to model marked temporal dynamics by using a mark-specific intensity function to explicitly capture the dependency between the mark and the time of the next event. Extensive experiments on two datasets demonstrate that the proposed method outperforms state-of-the-art methods at predicting marked temporal dynamics. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

Locally smoothed neural networks

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arXiv 2017年

作者： Pang, Liang Lan, Yanyan Xu, Jun Guo, Jiafeng Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Convolutional Neural networks (CNN) and the locally connected layer are limited in capturing the importance and relations of different local receptive fields, which are often crucial for tasks such as face verification, visual question answering, and word sequence prediction. To tackle the issue, we propose a novel locally smoothed neural network (LSNN) in this paper. The main idea is to represent the weight matrix of the locally connected layer as the product of the kernel and the smoother, where the kernel is shared over different local receptive fields, and the smoother is for determining the importance and relations of different local receptive fields. Specifically, a multi-variate Gaussian function is utilized to generate the smoother, for modeling the location relations among different local receptive fields. Furthermore, the content information can also be leveraged by setting the mean and precision of the Gaussian function according to the content. Experiments on some variant of MNIST clearly show our advantages over CNN and locally connected layer. Copyright © 2017, The Authors. All rights reserved.

关键词： Convolution

Rectangle transformation problem

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arXiv 2017年

作者： Wang, Shaojiang He, Kun Pan, Yicheng Xia, Mingji State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

In this paper, we propose the rectangle transformation problem (RTP) and its variants. RTP asks for a transformation by a rectangle partition between two rectangles of the same area. We are interested in the minimum RTP which requires to minimize the partition size. We mainly focus on the strict rectangle transformation problem (SRTP) in which rotation is not allowed in transforming. We show that SRTP has no finite solution if the ratio of the two parallel side lengths of input rectangles is irrational. So we turn to its complement denoted by SIRTP, in which case all side lengths can be assumed integral. We give a polynomial time algorithm ALGSIRTP which gives a solution at most q/p + O(√p) to SIRTP(p,q)(q ≥ p), where p and q are two integer side lengths of input rectangles p×q and q×p, and so ALGSIRTP is a O(√p)-approximation algorithm for minimum SIRTP(p,q). On the other hand, we show that there is not constant solution to SIRTP(p,q) for all integers p and q (q > p) even though the ratio q/p is within any constant range. We also raise a series of open questions for the research along this line. Copyright © 2017, The Authors. All rights reserved.

关键词： Polynomial approximation

Frequency synchronization for uplink massive MIMO with adaptive MUI suppression in angle-domain

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arXiv 2018年

作者： Ge, Yinghao Zhang, Weile Gao, Feifei Li, Geoffrey Ye MOE Key Lab for Intelligent Networks and Network Security School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an Shaanxi710049 China State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Automation Tsinghua University Beijing100084 China Department of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States

In this paper, we develop a novel angle-domain adaptive filtering (ADAF)-based frequency synchronization method for the uplink of a massive multiple-input multiple-output (MIMO) multiuser network, which is applicable for users with either separate or overlapped angle-of-arrival (AoA) regions. For each user, we first introduce the angle-constraining matrix (ACM), which consists of a set of selected match-filter (MF) beamformers pointing to the AoAs of the interested user. Then, the adaptive beamformer can be acquired by appropriately designing the ADAF vectors. Such beamformer can achieve two-stage adaptive multiuser interference (MUI) suppression, i.e., inherently suppressing the MUI from non-overlapping users by ACM in the first stage and substantially mitigating the MUI from adjacent overlapping users by ADAF vector in the second stage. For both separate and mutually overlapping users, the carrier frequency offset (CFO) estimation and subsequent data detection can be performed independently for each user, which significantly reduces computational complexity. Moreover, ADAF is rather robust to imperfect AoA knowledge and side cluster of scatterers, making itself promising for multiuser uplink transmission. We also analyze the performance of CFO estimation and obtain an approximated closed-form expression of mean-squared error (MSE). The effectiveness of the proposed method and its superiority over the existing methods are demonstrated by numerical results. Copyright © 2018, The Authors. All rights reserved.

关键词： Adaptive filters

Predicting links and their building lime: A path-based approach 30

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Predicting links and their building lime: A path-based appro...

30th AAAI Conference on Artificial Intelligence, AAAI 2016

作者： Li, Manling Jia, Yantao Wang, Yuanzhuo Zhao, Zeya Cheng, Xueqi Institute of Computing Technology Key lab of Network Data Science and Technology CAS Beijing100190 China

ISBN: (纸本)9781577357605

Predicting links and their building time in a knowledge network has been extensively studied in recent years. Most structure-based predictive methods consider structures and the time information of edges separately, which fail to characterize the correlation between them. In this paper, we propose a structure callcd the Time-Difference-labeled Path, and a link prediction method (TDLP). Experiments show that TDLP outperforms the state-of-the-art methods. Links and Building Time Prediction. © 2016, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Lime

EagleMine: Vision-guided mining in large graphs

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arXiv 2017年

作者： Feng, Wenjie Liu, Shenghua Faloutsos, Christos Hooi, Bryan Shen, Huawei Cheng, Xueqi Cas Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences School of Computer Science Carnegie Mellon University

Given a graph with millions of nodes, what patterns exist in the distributions of node characteristics, and how can we detect them and separate anomalous nodes in a way similar to human vision? In this paper, we propose a vision-guided algorithm, EagleMine, to summarize micro-cluster patterns in two-dimensional histogram plots constructed from node features in a large graph. EagleMine utilizes a water-level tree to capture cluster structures according to vision-based intuition at multi-resolutions. EagleMine traverses the waterlevel tree from the root and adopts statistical hypothesis tests to determine the optimal clusters that should be fitted along the path, and summarizes each cluster with a truncated Gaussian distribution. Experiments on real data show that our method can find truncated and overlapped elliptical clusters, even when some baseline methods split one visual cluster into pieces with Gaussian spheres. To identify potentially anomalous micro-clusters, EagleMine also a designates score to measure the suspiciousness of outlier groups (i.e. node clusters) and outlier nodes, detecting bots and anomalous users with high accuracy in the real Microblog data. Copyright © 2017, The Authors. All rights reserved.

关键词： Statistics

Layered Compression Scheme for Efficient data Collection of Sensory data

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Layered Compression Scheme for Efficient Data Collection of ...

International Conference on Computer Communication and networks

作者： Siguang Chen Shujun Zhang Bin Xu Jing Hu Vijayakumar Varadarajan Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and Telecommunications Nanjing China Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China Information Center Jiangsu China Tobacco Industry Co. Ltd. Nanjing China School of Computing Science and Engineering VIT University Chennai India

Although the existing compressed sensing (CS) based spatio-temporal data compression schemes can significantly decrease communication consumption for data collection, they ignore the data correlation among different clusters over spatial dimension. Actually, the discovery and utilization of spatial correlation among different clusters can further increase the compression rate (or improve the recovery effect). In this paper, we propose a layered compression scheme for efficient data collection of sensory data (LCS-EDC). In the proposed scheme, first, we design a multi-layer network architecture to support the exploration of spatio-temporal correlations, especially for exploring the spatial correlation among different clusters. And then, we construct the specific projection methods respectively for exploring the temporal correlation in sensory nodes, spatial correlation (intra-cluster) in cluster heads and spatial correlation (inter-cluster) in processing nodes. Meanwhile, the detailed solving method is developed to recover original data and achieve the approximate data collection in sink node. Finally, simulation results indicate that the proposed layered compression scheme has better recovery performance as compared with traditional clustered compression schemes (i.e., achieving efficient data collection with high quality).

关键词： Compressed sensing data correlation multi-layer architecture data collection recovery error

An Integrated Optimization Model for Urban Agglomeration Traffic and Land Use Based on Multi-objective Particle Swarm Optimization Algorithm

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An Integrated Optimization Model for Urban Agglomeration Tra...

IEEE International Conference on Intelligent Transportation Engineering (ICITE)

作者： Jiaqi Wu Yang Yu Wenqiang Xue Qinyuan Zhang College of Transportation science & Technology Nanjing Tech University Nanjing Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education Nanjing University of Posts and Telecommunications Nanjing YangZhou Urban Planning and Research Geography Information Information Center Hexi Campus International Department Jinling High School

Based on the mutual relationship between traffic and land use, the Integrated Transportation and Land Use Model (ITLM) is established from the perspective of urban agglomeration development. The model parameters are calibrated by using MATlab and ArcGIS in combination with information entropy model and Multi-objective Particle Swarm Optimization. From the macro and meso perspectives, the space syntax is used to quantify the transport links of cities in urban agglomerations. Taking the Yangtze River City Group as an example, combined with big data analysis by POI, after optimization of the land use area, population size, and traffic structure of various types of nature through the ITLM model, traffic occurrence and attraction of cities in urban agglomerations tend to be more balanced than the current situation. It plays a vital role in analyzing the current status of cities in urban agglomerations and promoting the further development of urban agglomerations.

关键词： Urban areas Transportation Sociology Statistics Mathematical model Optimization Particle swarm optimization