Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time. Anomaly detection in this context has the objective of identifying unusual temporal connections within the graph structure. Detecting edge anomalies in real time is crucial for mitigating potential risks. Unlike traditional anomaly detection, this task is particularly challenging due to concept drifts, large data volumes, and the need for real-time response. To face these challenges, we introduce ARES, an unsupervised anomaly detection framework for edge streams. ARES combines Graph Neural Networks (GNNs) for feature extraction with Half-Space Trees (HST) for anomaly scoring. GNNs capture both spike and burst anomalous behaviors within streams by embedding node and edge properties in a latent space, while HST partitions this space to isolate anomalies efficiently. ARES operates in an unsupervised way without the need for prior data labeling. To further validate its detection capabilities, we additionally incorporate a simple yet effective supervised thresholding mechanism. This approach leverages statistical dispersion among anomaly scores to determine the optimal threshold using a minimal set of labeled data, ensuring adaptability across different domains. We validate ARES through extensive evaluations across several real-world cyber-attack scenarios, comparing its performance against existing methods while analyzing its space and time complexity. The code used to perform the experiments is publicly available at https://github.com/AnomalyRecognitionModelForEdgeStreams/ARES.

Research in neural generative models for dynamic networks is constantly evolving, and sophisticated solutions have been exploited to characterize the long-term evolution of temporal graphs. Despite the efforts in the literature, state-of-the-art models face the problem of handling changes in the graph structure by relying on prior knowledge, compromising the model’s flexibility. In this paper, we propose a graph-size invariant probabilistic generative model, named $$\textrm{FuDGE}$$ , Fully Dynamic Graph Evolution, for predicting the graph evolution through step-wise changes in the graph structure. $$\textrm{FuDGE}$$ can generate evolving graphs by exploring the whole node space, thus ensuring fast and effective generation. We evaluate $$\textrm{FuDGE}$$ on real and synthetic benchmark datasets and compare its performance against state-of-the-art competitors. The results demonstrate that our approach offers a competitive advantage in generation and prediction quality compared to existing literature. The code is publicly available at https://github.com/FuDGE2023/fudge .

Simulating a recommendation system in a controlled environment, to identify specific behaviors and user preferences, requires highly flexible synthetic data generation models capable of mimicking the patterns and trends of real datasets.In this context, we propose HyDRA, a novel preferences data generation model driven by three main factors: user-item interaction level, item popularity, and user engagement level.The key innovations of the proposed process include the ability to generate user communities characterized by similar item adoptions, reflecting real-world social influences and trends.Additionally, HyDRA considers item popularity and user engagement as mixtures of different probability distributions, allowing for a more realistic simulation of diverse scenarios.This approach enhances the model's capacity to simulate a wide range of real-world cases, capturing the complexity and variability found in actual user behavior.We demonstrate the effectiveness of HyDRA through extensive experiments on well-known benchmark datasets.The results highlight its capability to replicate real-world data patterns, offering valuable insights for developing and testing recommendation systems in a controlled and realistic manner.The code used to perform the experiments is publicly available: https://github.com/flexibledatageneration/HYDRA.

User navigation on social media platforms is often driven by recommendation algorithms . A growing body of literature questions whether these recommendation systems may exacerbate detrimental phenomena, perpetrate intrinsic biases, and alter user preferences in the long-term. Driven by this premise, the present study formalizes the concept of “ algorithmic drift ”, further introducing a novel framework and two metrics to quantify it. Our methodology involves a simulation process that models user behavior through random walks , reflecting user navigation under the influence and guidance of recommendation systems. This approach highlights that each user may respond differently to such stimuli, varying in both resistance to recommendation influence and inertia in selecting new steps in the random walk. The proposed metrics measure the drift in user behavior and item consumption over time in the random walks. We conduct a comprehensive evaluation over both synthetic and real-world datasets to validate the framework’s ability to measure drift across different parameter settings. All code and data used in our experimentation are publicly accessible online. 1