While social networks greatly facilitate information dissemination, they are well known to have contributed to the phenomena of filter bubbles and echo chambers. This in turn can lead to societal polarization and erosion of trust in public institutions. Mitigating filter bubbles is an urgent open problem. Recently, approaches based on the influence maximization paradigm have been proposed in our community for mitigating filter bubbles by balancing exposure to opposing viewpoints. However, existing works ignore the inherent competition between the adoption of opposing viewpoints by users. In this paper, we propose a realistic model for the filter bubble problem, which unlike previous work, captures thecompetition between opposing opinions propagating in a network as well as thecomplementary nature of the reward forexposing users to both those opinions. We formulate an optimization problem for mitigating filter bubbles under our model. We establish several evidences of the intrinsic difficulty in developing constant approximation to the problem and develop a heuristic and two instance-dependent approximation algorithms. Our experiments over 4 real datasets show that our heuristic far outperforms two state-of-the-art baselines as well as other algorithms in both efficiency and mitigating filter bubbles. We also empirically demonstrate that our best heuristic performs close to the optimal objective, which is obtained by utilizing the theoretical bounds of our approximation algorithms.

A host of different KG embedding techniques have emerged recently and have been empirically shown to be very effective in accurately predicting missing facts in a KG, thus improving its coverage and quality. Unfortunately, embedding techniques can fall prey to adversarial data poisoning attack. In this form of attack, facts may be added to or deleted from a KG, called performing perturbations, that results in the manipulation of the plausibility of target facts in a KG. While recent works confirm this intuition, the attacks considered there ignore the risk of exposure. Intuitively, an attack is of limited value if it is highly likely to be caught, i.e., exposed. To address this, we introduce a notion of the exposure risk and propose a novel problem of attacking a KG by means of perturbations where the goal is to maximize the manipulation of the target fact's plausibility while keeping the risk of exposure under a given budget. We design a deep reinforcement learning-based framework, called RATA, that learns to use low-risk perturbations without compromising on the performance, i.e., manipulation of target fact plausibility. We test the performance of RATA against recently proposed strategies for KG attacks, on two different benchmark datasets and on different kinds of target facts. Our experiments show that RATA achieves state-of-the-art performance even while using a fraction of the risk.

Motivated by applications such as viral marketing, the problem of influence maximization (IM) has been extensively studied in the literature. The goal is to select a small number of users to adopt an item such that it results in a large cascade of adoptions by others. Existing works have three key limitations. (1) They do not account for economic considerations of a user in buying/adopting items. (2) Most studies on multiple items focus on competition, with complementary items receiving limited attention. (3) For the network owner, maximizing social welfare is important to ensure customer loyalty, which is not addressed in prior work in the IM literature. In this paper, we address all three limitations and propose a novel model called UIC that combines utility-driven item adoption with influence propagation over networks. Focusing on the mutually complementary setting, we formulate the problem of social welfare maximization in this novel setting. We show that while the objective function is neither submodular nor supermodular, surprisingly a simple greedy allocation algorithm achieves a factor of (1-1/e-ε) of the optimum expected social welfare. We develop bundleGRD, a scalable version of this approximation algorithm, and demonstrate, with comprehensive experiments on real and synthetic datasets, that it significantly outperforms all baselines.

In this paper, we study the problem of mining temporally anomalous sub-trajectory patterns from an input trajectory in a scalable manner. Given the prevailing road conditions, a sub-trajectory is temporally anomalous if its travel time deviates significantly from the expected time. Mining these patterns requires us to delve into the sub-trajectory space, which is not scalable for real-time analytics. To overcome this scalability challenge, we design a technique called MANTRA. We study the properties unique to anomalous sub-trajectories and utilize them in MANTRA to iteratively refine the search space into a disjoint set of sub-trajectory islands. The expensive enumeration of all possible sub-trajectories is performed only on the islands to compute the answer set of maximal anomalous sub-trajectories. Extensive experiments on both real and synthetic datasets establish MANTRA as more than 3 orders of magnitude faster than baseline techniques. Moreover, through trajectory classification and segmentation, we demonstrate that the proposed model conforms to human intuition.

The explosion in the availability of GPS-enabled devices has resulted in an abundance of trajectory data. In reality, however, majority of these trajectories are collected at a low sampling rate and only provide partial observations on their actually traversed routes. Consequently, they are mired with uncertainty. In this paper, we develop a technique called Infer Tra to infer uncertain trajectories from network-constrained partial observations. Rather than predicting the most likely route, the inferred uncertain trajectory takes the form of an edge-weighted graph and summarizes all probable routes in a holistic manner. For trajectory inference, Infer Tra employs Gibbs sampling by learning a Network Mobility Model (NMM) from a database of historical trajectories. Extensive experiments on real trajectory databases show that the graph-based approach of Infer Tra is up to 50% more accurate, 20 times faster, and immensely more versatile than state-of-the-art techniques.