Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition

Product reviews and ratings on e-commerce websites provide customers with detailed insights about various aspects of the product such as quality, usefulness, etc. Since they influence customers' buying decisions, product reviews have become a fertile ground for abuse by sellers (colluding with reviewers) to promote their own products or to tarnish the reputation of competitor's products. In this paper, our focus is on detecting such abusive entities (both sellers and reviewers) by applying tensor decomposition on the product reviews data. While tensor decomposition is mostly unsupervised, we formulate our problem as a semi-supervised binary multi-target tensor decomposition, to take advantage of currently known abusive entities. We empirically show that our multi-target semi-supervised model achieves higher precision and recall in detecting abusive entities as compared to unsupervised techniques. Finally, we show that our proposed stochastic partial natural gradient inference for our model empirically achieves faster convergence than stochastic gradient and Online-EM with sufficient statistics.Comment: Accepted to the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2019. Contains supplementary material. arXiv admin note: text overlap with arXiv:1804.0383

Tensor decomposition techniques for analysing time-varying networks

The aim of this Ph.D thesis is the study of time-varying networks via theoretical and data-driven approaches. Networks are natural objects to represent a vast variety of systems in nature, e.g., communication networks (phone calls and e-mails), online social networks (Facebook, Twitter), infrastructural networks, etc. Considering the temporal dimension of networks helps to better understand and predict complex phenomena, by taking into account both the fact that links in the network are not continuously active over time and the potential relation between multiple dimensions, such as space and time. A fundamental challenge in this area is the definition of mathematical models and tools able to capture topological and dynamical aspects and to reproduce properties observed on the real dynamics of networks. Thus, the purpose of this thesis is threefold: 1) we will focus on the analysis of the complex mesoscale patterns, as community like structures and their evolution in time, that characterize time-varying networks; 2) we will study how these patterns impact dynamical processes that occur over the network; 3) we will sketch a generative model to study the interplay between topological and temporal patterns of time-varying networks and dynamical processes occurring over the network, e.g., disease spreading. To tackle these problems, we adopt and extend an approach at the intersection between multi-linear algebra and machine learning: the decomposition of time-varying networks represented as tensors (multi-dimensional arrays). In particular, we focus on the study of Non-negative Tensor Factorization (NTF) techniques to detect complex topological and temporal patterns in the network. We first extend the NTF framework to tackle the problem of detecting anomalies in time-varying networks. Then, we propose a technique to approximate and reconstruct time-varying networks affected by missing information, to both recover the missing values and to reproduce dynamical processes on top of the network. Finally, we focus on the analysis of the interplay between the discovered patterns and dynamical processes. To this aim, we use the NTF as an hint to devise a generative model of time-varying networks, in which we can control both the topological and temporal patterns, to identify which of them has a major impact on the dynamics