Abstract This work introduces a data-centric framework for answering analytical questions using network models, transcending domain-specific modeling conventions. The unified network modeling framework (UNMF) provides an interdisciplinary strategy for principled modeling and analysis of complex networks, including multilayer and temporal networks that arise in sustainability-driven applications. UNMF connects dynamic analysis, pattern discovery, and network-grounded integration of heterogeneous sources (structured and unstructured). We present guided instantiations of UNMF in urban development, mobility, ecosystems, and social-network settings to show how explicit modeling choices can be documented, compared, and assessed within a shared evaluative framework. In doing so, we formalize and systematize Networked Data Science as a field at the intersection of network science and data science, and we define its scope and applications. This work contributes to network science by providing an auditable design-and-evaluation procedure for studying complex, evolving systems and for making representation choices more explicit, inspectable, and reusable across domains.

Abstract Individual and societal systems are open systems continuously affected by their situational context. In recent years, context sources have been increasingly considered in different domains to aid short and long-term forecasts of systems’ behavior. Nevertheless, available research generally disregards the role of prospective context, such as calendrical planning or weather forecasts. This work proposes a multiple-input neural architecture consisting of a sequential composition of long short-term memory units or temporal convolutional networks able to incorporate both historical and prospective sources of situational context to aid time series forecasting tasks. Considering urban case studies, we further assess the impact that different sources of external context have on medical emergency and mobility forecasts. Results show that the incorporation of external context variables, including calendrical and weather variables, can significantly reduce forecasting errors against state-of-the-art forecasters. In particular, the incorporation of prospective context, generally neglected in related work, mitigates error increases along the forecasting horizon.

Time series anomaly detection is an active research area, combining dozens of state-of-the-art methods that place heterogeneous views on what is an anomaly.This diversity of views -local and global, point and segment, univariate and multivariate, context-free and context-aware anomalies -is associated with moderate-to-high output divergences between methods.As a result, the user is faced with the difficult and laborious task of selecting the most appropriate methods and identifying cross-method consensus in an attempt to optimize recall and precision.Despite the relevance of establishing agreement criteria, existing principles are scarce and suffer from major problems: 1) show biases towards methods with correlated/redundant anomaly profiles; 2) depend on anomaly score thresholding; 3) prevent online detection; and 4) offer consensus not subjected to sound statistical testing.This work proposes UNIANO (UNIfied ANOmaly), an approach that combines simple yet effective empirical multivariate distribution statistics to address these drawbacks, guaranteeing a parameter-free and statistically robust integration of heterogeneous anomaly views.In this context, anomalies detected by less prevalent and concordant anomaly profiles, such as context-aware profiles in the presence of complementary variables, are not undervalued.Given a n-length time series and m views, UNIANO is aided by adequate data structures to achieve O(n log m 2 n) training time and linear O(m) testing-and-updating time.The gathered results confirm the relevance of the proposed approach.