Sourav S Bhowmick

Guozhong Li, Byron Choi, Jianliang Xu et al.

Time series shapelets are discriminative subsequences that have been recently found effective for time series clustering (TSC). The shapelets are convenient for interpreting the clusters. Thus, the main challenge for TSC is to discover high-quality variable-length shapelets to discriminate different clusters. In this paper, we propose a novel autoencoder-shapelet approach (AUTOSHAPE), which is the first study to take the advantage of both autoencoder and shapelet for determining shapelets in an unsupervised manner. An autoencoder is specially designed to learn high-quality shapelets. More specifically, for guiding the latent representation learning, we employ the latest self-supervised loss to learn the unified embeddings for variable-length shapelet candidates (time series subsequences) of different variables, and propose the diversity loss to select the discriminating embeddings in the unified space. We introduce the reconstruction loss to recover shapelets in the original time series space for clustering. Finally, we adopt Davies Bouldin index (DBI) to inform AUTOSHAPE of the clustering performance during learning. We present extensive experiments on AUTOSHAPE. To evaluate the clustering performance on univariate time series (UTS), we compare AUTOSHAPE with 15 representative methods using UCR archive datasets. To study the performance of multivariate time series (MTS), we evaluate AUTOSHAPE on 30 UEA archive datasets with 5 competitive methods. The results validate that AUTOSHAPE is the best among all the methods compared. We interpret clusters with shapelets, and can obtain interesting intuitions about clusters in two UTS case studies and one MTS case study, respectively.

Zifeng Yuan, Huey Eng Chua, Sourav S Bhowmick et al.

Canned patterns (i.e. small subgraph patterns) in visual graph query interfaces (a.k.a GUI) facilitate efficient query formulation by enabling pattern-at-a-time construction mode. However, existing GUIs for querying large networks either do not expose any canned patterns or if they do then they are typically selected manually based on domain knowledge. Unfortunately, manual generation of canned patterns is not only labor intensive but may also lack diversity for supporting efficient visual formulation of a wide range of subgraph queries. In this paper, we present a novel generic and extensible framework called TATTOO that takes a data-driven approach to automatically selecting canned patterns for a GUI from large networks. Specifically, it first decomposes the underlying network into truss-infested and truss-oblivious regions. Then candidate canned patterns capturing different real-world query topologies are generated from these regions. Canned patterns based on a user-specified plug are then selected for the GUI from these candidates by maximizing coverage and diversity, and by minimizing the cognitive load of the pattern set. Experimental studies with real-world datasets demonstrate the benefits of TATTOO. Importantly, this work takes a concrete step towards realizing plug-and-play visual graph query interfaces for large networks.

Hui Li, Mengting Xu, Sourav S Bhowmick et al.

Since its introduction in 2003, the influence maximization (IM) problem has drawn significant research attention in the literature. The aim of IM is to select a set of k users who can influence the most individuals in the social network. The problem is proven to be NP-hard. A large number of approximate algorithms have been proposed to address this problem. The state-of-the-art algorithms estimate the expected influence of nodes based on sampled diffusion paths. As the number of required samples have been recently proven to be lower bounded by a particular threshold that presets tradeoff between the accuracy and efficiency, the result quality of these traditional solutions is hard to be further improved without sacrificing efficiency. In this paper, we present an orthogonal and novel paradigm to address the IM problem by leveraging deep learning models to estimate the expected influence. Specifically, we present a novel framework called DISCO that incorporates network embedding and deep reinforcement learning techniques to address this problem. Experimental study on real-world networks demonstrates that DISCO achieves the best performance w.r.t efficiency and influence spread quality compared to state-of-the-art classical solutions. Besides, we also show that the learning model exhibits good generality.