publications

2021

1. NeurIPS

   Detecting Anomalous Event Sequences with Temporal Point Processes

   Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, and Stephan Günnemann

   In Neural Information Processing Systems, NeurIPS 2021

   Abs arXiv Code

   Automatically detecting anomalies in event data can provide substantial value in domains such as healthcare, DevOps, and information security. In this paper, we frame the problem of detecting anomalous continuous-time event sequences as out-of-distribution (OoD) detection for temporal point processes (TPPs). First, we show how this problem can be approached using goodness-of-fit (GoF) tests. We then demonstrate the limitations of popular GoF statistics for TPPs and propose a new test that addresses these shortcomings. The proposed method can be combined with various TPP models, such as neural TPPs, and is easy to implement. In our experiments, we show that the proposed statistic excels at both traditional GoF testing, as well as at detecting anomalies in simulated and real-world data.

2. IJCAI

   Neural Temporal Point Processes: A Review

   Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, and Stephan Günnemann

   In International Joint Conference on Artificial Intelligence, IJCAI 2021

   Abs arXiv Talk

   Temporal point processes (TPP) are probabilistic generative models for continuous-time event sequences. Neural TPPs combine the fundamental ideas from point process literature with deep learning approaches, thus enabling construction of flexible and efficient models. The topic of neural TPPs has attracted significant attention in the recent years, leading to the development of numerous new architectures and applications for this class of models. In this review paper we aim to consolidate the existing body of knowledge on neural TPPs. Specifically, we focus on important design choices and general principles for defining neural TPP models. Next, we provide an overview of application areas commonly considered in the literature. We conclude this survey with the list of open challenges and important directions for future work in the field of neural TPPs.

2020

1. NeurIPS Oral

   Fast and Flexible Temporal Point Processes with Triangular Maps

   Oleksandr Shchur, Nicholas Gao, Marin Biloš, and Stephan Günnemann

   In Neural Information Processing Systems, NeurIPS 2020

   Abs arXiv Code

   Temporal point process (TPP) models combined with recurrent neural networks provide a powerful framework for modeling continuous-time event data. While such models are flexible, they are inherently sequential and therefore cannot benefit from the parallelism of modern hardware. By exploiting the recent developments in the field of normalizing flows, we design TriTPP — a new class of non-recurrent TPP models, where both sampling and likelihood computation can be done in parallel. TriTPP matches the flexibility of RNN-based methods but permits orders of magnitude faster sampling. This enables us to use the new model for variational inference in continuous-time discrete-state systems. We demonstrate the advantages of the proposed framework on synthetic and real-world datasets.

2. ICLR Spotlight

   Intensity-free Learning of Temporal Point Processes

   Oleksandr Shchur*, Marin Biloš*, and Stephan Günnemann

   In International Conference on Learning Representations, ICLR 2020

   Abs arXiv Code

   Temporal point processes are the dominant paradigm for modeling sequences of events happening at irregular intervals. The standard way of learning in such models is by estimating the conditional intensity function. However, parameterizing the intensity function usually incurs several trade-offs. We show how to overcome the limitations of intensity-based approaches by directly modeling the conditional distribution of inter-event times. We draw on the literature on normalizing flows to design models that are flexible and efficient. We additionally propose a simple mixture model that matches the flexibility of flow-based models, but also permits sampling and computing moments in closed form. The proposed models achieve state-of-the-art performance in standard prediction tasks and are suitable for novel applications, such as learning sequence embeddings and imputing missing data.

3. DLG

   Overlapping Community Detection with Graph Neural Networks

   Oleksandr Shchur, and Stephan Günnemann

   In Deep Learning for Graphs Workshop, DLG 2020

   Abs arXiv Code

   Community detection is a fundamental problem in machine learning. While deep learning has shown great promise in many graphrelated tasks, developing neural models for community detection has received surprisingly little attention. The few existing approaches focus on detecting disjoint communities, even though communities in real graphs are well known to be overlapping. We address this shortcoming and propose a graph neural network (GNN) based model for overlapping community detection. Despite its simplicity, our model outperforms the existing baselines by a large margin in the task of community recovery. We establish through an extensive experimental evaluation that the proposed model is effective, scalable and robust to hyperparameter settings. We also perform an ablation study that confirms that GNN is the key ingredient to the power of the proposed model.

2019

1. GEM

   Dual-primal Graph Convolutional Networks

   Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski, Or Litany, Stephan Günnemann, and Michael M Bronstein

   In Graph Embedding and Mining Workshop, GEM 2019

   Abs arXiv

   In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture that alternates convolution-like operations on the graph and its dual. Our approach allows to learn both vertex- and edge features and generalizes the previous graph attention (GAT) model. We provide extensive experimental validation showing state-of-the-art results on a variety of tasks tested on established graph benchmarks, including CORA and Citeseer citation networks as well as MovieLens, Flixter, Douban and Yahoo Music graph-guided recommender systems.

2018

1. ICML Long oral

   NetGAN: Generating Graphs via Random Walks

   Aleksandar Bojchevski*, Oleksandr Shchur*, Daniel Zügner*, and Stephan Günnemann

   In International Conference on Machine Learning, ICML 2018

   Abs arXiv Code

   We propose NetGAN – the first implicit generative model for graphs able to mimic real-world networks. We pose the problem of graph generation as learning the distribution of biased random walks over the input graph. The proposed model is based on a stochastic neural network that generates discrete output samples and is trained using the Wasserstein GAN objective. NetGAN is able to produce graphs that exhibit well-known network patterns without explicitly specifying them in the model definition. At the same time, our model exhibits strong generalization properties, as highlighted by its competitive link prediction performance, despite not being trained specifically for this task. Being the first approach to combine both of these desirable properties, NetGAN opens exciting avenues for further research.

2. R2L

   Pitfalls of Graph Neural Network Evaluation

   Oleksandr Shchur*, Maximilian Mumme*, Aleksandar Bojchevski, and Stephan Günnemann

   In Relational Representation Learning Workshop, R2L 2018

   Abs arXiv Code

   Semi-supervised node classification in graphs is a fundamental problem in graph mining, and the recently proposed graph neural networks (GNNs) have achieved unparalleled results on this task. Due to their massive success, GNNs have attracted a lot of attention, and many novel architectures have been put forward. In this paper we show that existing evaluation strategies for GNN models have serious shortcomings. We show that using the same train/validation/test splits of the same datasets, as well as making significant changes to the training procedure (e.g. early stopping criteria) precludes a fair comparison of different architectures. We perform a thorough empirical evaluation of four prominent GNN models and show that considering different splits of the data leads to dramatically different rankings of models. Even more importantly, our findings suggest that simpler GNN architectures are able to outperform the more sophisticated ones if the hyperparameters and the training procedure are tuned fairly for all models.

3. ICDMW

   Anomaly Detection in Car-Booking Graphs

   Oleksandr Shchur, Aleksandar Bojchevski, Mohamed Farghal, Stephan Günnemann, and Yusuf Saber

   In International Conference on Data Mining Workshops, ICDM 2018

   Abs HTML

   The use of car-booking services has gained massive popularity in the recent years – which led to an increasing number of fraudsters that try to game these systems. In this paper we describe a framework for fraud detection in car-booking systems. Our core idea lies in casting this problem as an instance of anomaly detection in temporal graphs. Specifically, we use unsupervised techniques, such as dense subblock discovery, to detect suspicious activity. The proposed framework is able to adapt to the variations in the data inherent to the car-booking setting, and detects fraud with high precision. This work is performed in collaboration with Careem, where the described framework is currently being deployed in production.