Data Mining and Knowledge Discovery最新文献

Abstract

The detection of central nodes in a network is a fundamental task in network science and graph data analysis. During the past decades, numerous centrality measures have been presented to characterize what is a central node. However, few studies address this issue from a statistical inference perspective. In this paper, we formulate the central node identification issue as a weighted kernel density estimation problem on graphs. Such a formulation provides a generic framework for recognizing central nodes. On one hand, some existing centrality evaluation metrics can be unified under this framework through the manipulation of kernel functions. On the other hand, more effective methods for node centrality assessment can be developed based on proper weighting coefficient specification. Experimental results on 20 simulated networks and 53 real networks show that our method outperforms both six prior state-of-the-art centrality measures and two recently proposed centrality evaluation methods. To the best of our knowledge, this is the first piece of work that addresses the central node identification issue via weighted kernel density estimation.

Although knowledge graphs store a large number of facts in the form of triplets, they are still limited by incompleteness. Hence, Knowledge Graph Completion (KGC), defined as inferring missing entities or relations based on observed facts, has long been a fundamental issue for various knowledge driven downstream applications. Prevailing KG embedding methods for KGC like TransE rely solely on mining structural information of existing facts, thus failing to handle generalization issue as they are inapplicable to unseen entities. Recently, a series of researches employ pre-trained encoders to learn textual representation for triples i.e., textual-encoding methods. While exhibiting great generalization for unseen entities, they are still inferior compared with above KG embedding based ones. In this paper, we devise a novel textual-encoding learning framework for KGC. To enrich textual prior knowledge for more informative prediction, it features three hierarchical maskings which can utilize far contexts of input text so that textual prior knowledge can be elicited. Besides, to solve predictive ambiguity caused by improper relational modeling, a relational-aware structure learning scheme is applied based on textual embeddings. Extensive experimental results on several popular datasets suggest the effectiveness of our approach even compared with recent state-of-the-arts in this task.

Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., by issuing an alarm or by updating a learning algorithm. However, detecting changes is challenging when observations are high-dimensional. In high-dimensional data, change detectors should not only be able to identify when changes happen, but also in which subspace they occur. Ideally, one should also quantify how severe they are. Our approach, ABCD, has these properties. ABCD learns an encoder-decoder model and monitors its accuracy over a window of adaptive size. ABCD derives a change score based on Bernstein’s inequality to detect deviations in terms of accuracy, which indicate changes. Our experiments demonstrate that ABCD outperforms its best competitor by up to 20% in F1-score on average. It can also accurately estimate changes’ subspace, together with a severity measure that correlates with the ground truth.

With the rise of Web 2.0 platforms such as online social media, people’s private information, such as their location, occupation and even family information, is often inadvertently disclosed through online discussions. Therefore, it is important to detect such unwanted privacy disclosures to help alert people affected and the online platform. In this paper, privacy disclosure detection is modeled as a multi-label text classification (MLTC) problem, and a new privacy disclosure detection model is proposed to construct an MLTC classifier for detecting online privacy disclosures. This classifier takes an online post as the input and outputs multiple labels, each reflecting a possible privacy disclosure. The proposed presentation method combines three different sources of information, the input text itself, the label-to-text correlation and the label-to-label correlation. A double-attention mechanism is used to combine the first two sources of information, and a graph convolutional network is employed to extract the third source of information that is then used to help fuse features extracted from the first two sources of information. Our extensive experimental results, obtained on a public dataset of privacy-disclosing posts on Twitter, demonstrated that our proposed privacy disclosure detection method significantly and consistently outperformed other state-of-the-art methods in terms of all key performance indicators.

The term Behavioral Networks describes networks that contain relational information on human behavior. This ranges from social networks that contain friendships or cooperations between individuals, to navigational networks that contain geographical or web navigation, and many more. Understanding the forces driving behavior within these networks can be beneficial to improving the underlying network, for example, by generating new hyperlinks on websites, or by proposing new connections and friends on social networks. Previous approaches considered different hypotheses on a single network and evaluated which hypothesis fits best. These hypotheses can represent human intuition and expert opinions or be based on previous insights. In this work, we extend these approaches to enable the comparison of a single hypothesis between multiple networks. We unveil several issues of naive approaches that potentially impact comparisons and lead to undesired results. Based on these findings, we propose a framework with five flexible components that allow addressing specific analysis goals tailored to the application scenario. We show the benefits and limits of our approach by applying it to synthetic data and several real-world datasets, including web navigation, bibliometric navigation, and geographic navigation. Our work supports practitioners and researchers with the aim of understanding similarities and differences in human behavior between environments.

Abstract

The ranking of objects is widely used to rate their relative quality or relevance across multiple assessments. Beyond classical rank aggregation, it is of interest to estimate the usually unobservable latent signals that inform a consensus ranking. Under the only assumption of independent assessments, which can be incomplete, we introduce indirect inference via convex optimization in combination with computationally efficient Poisson Bootstrap. Two different objective functions are suggested, one linear and the other quadratic. The mathematical formulation of the signal estimation problem is based on pairwise comparisons of all objects with respect to their rank positions. Sets of constraints represent the order relations. The transitivity property of rank scales allows us to reduce substantially the number of constraints associated with the full set of object comparisons. The key idea is to globally reduce the errors induced by the rankers until optimal latent signals can be obtained. Its main advantage is low computational costs, even when handling (n < < p) data problems. Exploratory tools can be developed based on the bootstrap signal estimates and standard errors. Simulation evidence, a comparison with the state-of-the-art rank centrality method, and two applications, one in higher education evaluation and the other in molecular cancer research, are presented.

We propose the use of a deep learning architecture, called RETINA, to predict multi-alternative, multi-attribute consumer choice from eye movement data. RETINA directly uses the complete time series of raw eye-tracking data from both eyes as input to state-of-the art Transformer and Metric Learning Deep Learning methods. Using the raw data input eliminates the information loss that may result from first calculating fixations, deriving metrics from the fixations data and analysing those metrics, as has been often done in eye movement research, and allows us to apply Deep Learning to eye tracking data sets of the size commonly encountered in academic and applied research. Using a data set with 112 respondents who made choices among four laptops, we show that the proposed architecture outperforms other state-of-the-art machine learning methods (standard BERT, LSTM, AutoML, logistic regression) calibrated on raw data or fixation data. The analysis of partial time and partial data segments reveals the ability of RETINA to predict choice outcomes well before participants reach a decision. Specifically, we find that using a mere 5 s of data, the RETINA architecture achieves a predictive validation accuracy of over 0.7. We provide an assessment of which features of the eye movement data contribute to RETINA’s prediction accuracy. We make recommendations on how the proposed deep learning architecture can be used as a basis for future academic research, in particular its application to eye movements collected from front-facing video cameras.

Session-based recommendation (SR) aims to dynamically recommend items to a user based on a sequence of the most recent user-item interactions. Most existing studies on SR adopt advanced deep learning methods. However, the majority only consider a special behavior type (e.g., click), while those few considering multi-typed behaviors ignore to take full advantage of the relationships between products (items). In this case, the paper proposes a novel approach, called Substitutable and Complementary Relationships from Multi-behavior Data (denoted as SCRM) to better explore the relationships between products for effective recommendation. Specifically, we firstly construct substitutable and complementary graphs based on a user’s sequential behaviors in every session by jointly considering ‘click’ and ‘purchase’ behaviors. We then design a denoising network to remove false relationships, and further consider constraints on the two relationships via a particularly designed loss function. Extensive experiments on two e-commerce datasets demonstrate the superiority of our model over state-of-the-art methods, and the effectiveness of every component in SCRM.

Random walk with restart (RWR) is a widely-used measure of node similarity in graphs, and it has proved useful for ranking, community detection, link prediction, anomaly detection, etc. Since RWR is typically required to be computed separately for a larger number of query nodes or even for all nodes, fast computation of it is indispensable. However, for hypergraphs, the fast computation of RWR has been unexplored, despite its great potential. In this paper, we propose ARCHER, a fast computation framework for RWR on hypergraphs. Specifically, we first formally define RWR on hypergraphs, and then we propose two computation methods that compose ARCHER. Since the two methods are complementary (i.e., offering relative advantages on different hypergraphs), we also develop a method for automatic selection between them, which takes a very short time compared to the total running time. Through our extensive experiments on 18 real-world hypergraphs, we demonstrate (a) the speed and space efficiency of ARCHER, (b) the complementary nature of the two computation methods composing ARCHER, (c) the accuracy of its automatic selection method, and (d) its successful application to anomaly detection on hypergraphs.