The problem of online social network manipulation for community canvassing is of real concern in today's world. Motivated by the study of voter models, opinion and polarization dynamics on networks, we model community canvassing as a dynamic process over a network enabled via gradient-based attacks on GNNs. Existing attacks on GNNs are all single-step and do not account for the dynamic cascading nature of information diffusion in networks. We consider the realistic scenario where an adversary uses a GNN as a proxy to predict and manipulate voter preferences, especially uncertain voters. Gradient-based attacks on the GNN inform the adversary of strategic manipulations that can be made to proselytize targeted voters. In particular, we explore $\textit{minimum budget attacks for community canvassing}$ (MBACC). We show that the MBACC problem is NP-Hard and propose Dynamic Multi-Step Adversarial Community Canvassing (MAC) to address it. MAC makes dynamic local decisions based on the heuristic of low budget and high second-order influence to convert and perturb target voters. MAC is a dynamic multi-step attack that discovers low-budget and high-influence targets from which efficient cascading attacks can happen. We evaluate MAC against single-step baselines on the MBACC problem with multiple underlying networks and GNN models. Our experiments show the superiority of MAC which is able to discover efficient multi-hop attacks for adversarial community canvassing. Our code implementation and data is available at https://github.com/saurabhsharma1993/mac.

The problem of long-tailed recognition (LTR) has received attention in recent years due to the fundamental power-law distribution of objects in the real-world. Most recent works in LTR use softmax classifiers that are biased in that they correlate classifier norm with the amount of training data for a given class. In this work, we show that learning prototype classifiers addresses the biased softmax problem in LTR. Prototype classifiers can deliver promising results simply using Nearest-Class- Mean (NCM), a special case where prototypes are empirical centroids. We go one step further and propose to jointly learn prototypes by using distances to prototypes in representation space as the logit scores for classification. Further, we theoretically analyze the properties of Euclidean distance based prototype classifiers that lead to stable gradient-based optimization which is robust to outliers. To enable independent distance scales along each channel, we enhance Prototype classifiers by learning channel-dependent temperature parameters. Our analysis shows that prototypes learned by Prototype classifiers are better separated than empirical centroids. Results on four LTR benchmarks show that Prototype classifier outperforms or is comparable to state-of-the-art methods. Our code is made available at https://github.com/saurabhsharma1993/prototype-classifier-ltr.

Twitter, used in 200 countries with over 250 milliontweets a day, is a rich source of local news from aroundthe world. Many events of local importance are first reportedon Twitter, including many that never reach newschannels. Further, there are often only a few tweetsreporting each such event, in contrast with the largervolumes that follow events of wider significance. Eventhough such events may be primarily of local importance,they can also be of critical interest to some specificbut possibly far flung entities: For example, a firein a supplier’s factory half-way around the world maybe of interest even from afar. In this paper we describehow this ‘long tail’ of events can be detected in spite oftheir sparsity.We then extract and correlate informationfrom multiple tweets describing the same event. Ourgeneric architecture for converting a tweet-stream intoevent-objects uses locality sensitive hashing, classification,boosting, information extraction and clustering.Our results, based on millions of tweets monitored overmany months, appear to validate our approach and architecture:We achieved success-rates in the 80% rangefor event detection and 76% on event-correlation; we also reduced tweet-comparisons by 80% using LSH.