BINet: Multi-perspective Business Process Anomaly Classification

In this paper, we introduce BINet, a neural network architecture for real-time multi-perspective anomaly detection in business process event logs. BINet is designed to handle both the control flow and the data perspective of a business process. Additionally, we propose a set of heuristics for setting the threshold of an anomaly detection algorithm automatically. We demonstrate that BINet can be used to detect anomalies in event logs not only on a case level but also on event attribute level. Finally, we demonstrate that a simple set of rules can be used to utilize the output of BINet for anomaly classification. We compare BINet to eight other state-of-the-art anomaly detection algorithms and evaluate their performance on an elaborate data corpus of 29 synthetic and 15 real-life event logs. BINet outperforms all other methods both on the synthetic as well as on the real-life datasets

Contextual Centrality: Going Beyond Network Structures

Centrality is a fundamental network property which ranks nodes by their structural importance. However, structural importance may not suffice to predict successful diffusions in a wide range of applications, such as word-of-mouth marketing and political campaigns. In particular, nodes with high structural importance may contribute negatively to the objective of the diffusion. To address this problem, we propose contextual centrality, which integrates structural positions, the diffusion process, and, most importantly, nodal contributions to the objective of the diffusion. We perform an empirical analysis of the adoption of microfinance in Indian villages and weather insurance in Chinese villages. Results show that contextual centrality of the first-informed individuals has higher predictive power towards the eventual adoption outcomes than other standard centrality measures. Interestingly, when the product of diffusion rate $p$ and the largest eigenvalue $\lambda_1$ is larger than one and diffusion period is long, contextual centrality linearly scales with eigenvector centrality. This approximation reveals that contextual centrality identifies scenarios where a higher diffusion rate of individuals may negatively influence the cascade payoff. Further simulations on the synthetic and real-world networks show that contextual centrality has the advantage of selecting an individual whose local neighborhood generates a high cascade payoff when $p \lambda_1 < 1$. Under this condition, stronger homophily leads to higher cascade payoff. Our results suggest that contextual centrality captures more complicated dynamics on networks and has significant implications for applications, such as information diffusion, viral marketing, and political campaigns

Game theoretic modeling of AIMD network equilibrium

This paper deals with modeling of network’s dynamic using game theory approach. The process of interaction among players (network users), trying to maximize their payoffs (e.g. throughput) could be analyzed using game-based concepts (Nash equilibrium, Pareto efficiency, evolution stability etc.). In this work we presented the model of TCP network’s dynamic and proved existence and uniqueness of solution, formulated payoff matrix for a network game and found conditions of equilibrium existence depending of loss sensitivity parameter. We consider influence if denial of service attacks on the equilibrium characteristics and illustrate results by simulations.В данной работе исследуется моделирования динамики сети на основе теоретико-игрового подхода. Процесс взаимодействия между пользоватлями, которые пытаются максимизировать свои выигрыши (например, долю сети) допускает представление в форме игры и применение методов анализа равновесия. В работе предлагается модель TCP сети и доказано существование и единственность точки устойчивого распределения ресурсов, построена матрица сетевой игры и найдены условия существования равновесия в зависимости от чувствительности пользователей к наличию ошибок. Рассмотрены также влияние атак на характеристики равновесия и проведено имитационное моделирование.В даній роботі досліджується моделювання динаміки мережі на основі теоретико-ігрового підходу. Процес взаємодії між користувачами, що намагаються максимізувати свої виграші (наприклад, частку мережі) допускає представлення у формі гри та застосування методів аналізу рівноваги. В роботі пропонується модель TCP мережі та доведено існування і єдність точки стійкого розподілу ресурсів, побудована матриця мережевої гри та знайдені умови існування рівноваги в залежності від чутливості користувачів до наявності помилок. Розглянуто також вплив атак на характеристики рівноваги та проведене імітаційне моделювання