Data Clustering Aggarwal, Charu C; Reddy, Chandan K
2014, 2013, 2018-09-03, 2013-08-21, Letnik:
31
eBook
In this book, top researchers from around the world cover the entire area of clustering, from basic methods to more refined and complex data clustering approaches. They pay special attention to ...recent issues in graphs, social networks, and other domains. The book explores the characteristics of clustering problems in a variety of application areas. It also explains how to glean detailed insight from the clustering process--including how to verify the quality of the underlying clusters--through supervision, human intervention, or the automated generation of alternative clusters.
Outlier Detection for Temporal Data: A Survey Gupta, Manish; Jing Gao; Aggarwal, Charu C. ...
IEEE transactions on knowledge and data engineering,
2014-Sept., 2014-9-00, 20140901, Letnik:
26, Številka:
9
Journal Article
Recenzirano
In the statistics community, outlier detection for time series data has been studied for decades. Recently, with advances in hardware and software technology, there has been a large body of work on ...temporal outlier detection from a computational perspective within the computer science community. In particular, advances in hardware technology have enabled the availability of various forms of temporal data collection mechanisms, and advances in software technology have enabled a variety of data management mechanisms. This has fueled the growth of different kinds of data sets such as data streams, spatio-temporal data, distributed streams, temporal networks, and time series data, generated by a multitude of applications. There arises a need for an organized and detailed study of the work done in the area of outlier detection with respect to such temporal datasets. In this survey, we provide a comprehensive and structured overview of a large set of interesting outlier definitions for various forms of temporal data, novel techniques, and application scenarios in which specific definitions and techniques have been widely used.
Attributed Graph Force Learning Sun, Ke; Xia, Feng; Liu, Jiaying ...
IEEE transaction on neural networks and learning systems,
2024-April, 2024-Apr, 2024-4-00, 20240401, Letnik:
35, Številka:
4
Journal Article
In numerous network analysis tasks, feature representation plays an imperative role. Due to the intrinsic nature of networks being discrete, enormous challenges are imposed on their effective usage. ...There has been a significant amount of attention on network feature learning in recent times that has the potential of mapping discrete features into a continuous feature space. The methods, however, lack preserving the structural information owing to the utilization of random negative sampling during the training phase. The ability to effectively join attribute information to embedding feature space is also compromised. To address the shortcomings identified, a novel attribute force-based graph (AGForce) learning model is proposed that keeps the structural information intact along with adaptively joining attribute information to the node's features. To demonstrate the effectiveness of the proposed framework, comprehensive experiments on benchmark datasets are performed. AGForce based on the spring-electrical model extends opportunities to simulate node interaction for graph learning.
Network alignment aims at inferring a set of anchor links matching the shared entities between different information networks, which has become a prerequisite step for effective fusion of multiple ...information networks. In this paper, we will study the network alignment problem to fuse online social networks specifically. Social network alignment is extremely challenging to address due to several reasons, i.e., lack of training data , network heterogeneity and one-to-one constraint . Existing network alignment works usually require a large number of training instances, but such a demand can hardly be met in applications, as manual anchor link labeling is extremely expensive. Significantly different from other homogeneous network alignment works, information in online social networks is usually of heterogeneous categories, the incorporation of which in model building is not an easy task. Furthermore, the one-to-one cardinality constraint on anchor links renders their inference process intertwistingly correlated. To resolve these three challenges, a novel network alignment model, namely ActiveIter (Active Iterative Alignment), is introduced in this paper. The model ActiveIter defines a set of inter-network meta diagrams for anchor link feature extraction, adopts active learning for effective label query and uses greedy link selection for anchor link cardinality filtering. Extensive experiments were performed on a real-world aligned networks dataset, and the experimental results have demonstrated the effectiveness of ActiveIter compared with other state-of-the-art baseline methods.
Temporal graph learning has attracted great attention with its ability to deal with dynamic graphs. Although current methods are reasonably accurate, most of them are unexplainable due to their ...black-box nature. It remains a challenge to explain how temporal graph learning models adapt to information evolution. Furthermore, with the increasing application of artificial intelligence in various scientific domains, such as chemistry and biomedicine, the importance of delivering not only precise outcomes but also offering explanations regarding the learning models becomes paramount. This transparency aids users in comprehending the decision-making procedures and instills greater confidence in the generated models. To address this issue, this article proposes a novel physics-informed explainable continual learning (PiECL), focusing on temporal graphs. Our proposed method utilizes physical and mathematical algorithms to quantify the disturbance of new data to previous knowledge for obtaining changed information over time. As the proposed model is based on theories in physics, it can provide a transparent underlying mechanism for information evolution detection, thus enhancing explainability. The experimental results on three real-world datasets demonstrate that PiECL can explain the learning process, and the generated model outperforms other state-of-the-art methods. PiECL shows tremendous potential for explaining temporal graph learning in various scientific contexts.
In recent years, a number of indirect data collection methodologies have lead to the proliferation of uncertain data. Such data points are often represented in the form of a probabilistic function, ...since the corresponding deterministic value is not known. This increases the challenge of mining and managing uncertain data, since the precise behavior of the underlying data is no longer known. In this paper, we provide a survey of uncertain data mining and management applications. In the field of uncertain data management, we will examine traditional methods such as join processing, query processing, selectivity estimation, OLAP queries, and indexing. In the field of uncertain data mining, we will examine traditional mining problems such as classification and clustering. We will also examine a general transform based technique for mining uncertain data. We discuss the models for uncertain data, and how they can be leveraged in a variety of applications. We discuss different methodologies to process and mine uncertain data in a variety of forms.
Two goals of network science are to (i) uncover fundamental properties of phenomena modeled as networks, and to (ii) explore novel use of networks as models for a diverse range of systems and ...phenomena in order to improve our understanding of such systems and phenomena. This paper advances the latter direction by casting credibility estimation in social sensing applications as a network science problem, and by presenting a network model that helps understand the fundamental accuracy trade-offs of a credibility estimator. Social sensing refers to data collection scenarios, where observations are collected from (possibly unvetted) human sources. We call such observations claims to emphasize that we do not know whether or not they are factually correct. Predictable, scalable and robust estimation of both source reliability and claim correctness, given neither in advance, becomes a key challenge given the unvetted nature of sources and lack of means to verify their claims. In a previous conference publication, we proposed a maximum likelihood approach to jointly estimate both source reliability and claim correctness. We also derived confidence bounds to quantify the accuracy of such estimation. In this paper, we cast credibility estimation as a network science problem and offer systematic sensitivity analysis of the optimal estimator to understand its fundamental accuracy trade-offs as a function of an underlying network topology that describes key problem space parameters. It enables assured social sensing, where not only source reliability and claim correctness are estimated, but also the accuracy of such estimates is correctly predicted for the problem at hand.
Outlier detection algorithms are often computationally intensive because of their need to score each point in the data. Even simple distance-based algorithms have quadratic complexity. ...High-dimensional outlier detection algorithms such as subspace methods are often even more computationally intensive because of their need to explore different subspaces of the data. In this paper, we propose an exceedingly simple subspace outlier detection algorithm, which can be implemented in a few lines of code, and whose complexity is
linear
in the size of the data set and the space requirement is
constant
. We show that this outlier detection algorithm is much faster than both conventional and high-dimensional algorithms and also provides more accurate results. The approach uses randomized hashing to score data points and has a neat subspace interpretation. We provide a visual representation of this interpretability in terms of
outlier sensitivity histograms
. Furthermore, the approach can be easily generalized to data streams, where it provides an efficient approach to discover outliers in real time. We present experimental results showing the effectiveness of the approach over other state-of-the-art methods.
Sensor networks are often redundant by design in order to achieve reliability in information processing. In many cases, the relationships between the different sensors are known a-priori, and can be ...represented as virtual linkages among the different sensors. These virtual linkages correspond to an information network of sensors, which provides useful external input to the problem of sensor selection. In this paper, we propose the unique approach of using external linkage information in order to improve the efficiency of very large scale sensor selection. We design efficient theoretical models, including a greedy approximation algorithm and an integer programming formulation for sensor selection. Our greedy selection algorithm provides an approximation bound of 1−1/e, where e is the base of the natural logarithm. We show that our approach is much more effective than baseline sampling strategies. We present experimental results that illustrate the effectiveness and efficiency of our approach.
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