This paper investigates how the parameters governing network structure vary over time across a sequence of observed dynamic networks. We focus on ensembles of networks that exhibit sudden structural changes at certain time points but remain stable or undergo minimal changes in between. Many real-world networks, such as international trade networks and political co-voting networks, display this behaviour. Given a sequence of observed networks, our objective is to estimate the corresponding time-varying parameters of an exponential random graph model (ERGM) and identify periods of parameter stability as well as abrupt changes in the underlying network-generating mechanisms. To this end, we introduce a novel fused lasso exponential random graph model (FL-ERGM), which encourages similar parameter values across consecutive time points while allowing abrupt changes when supported by the data. To address the intractability of the ERGM likelihood, we adopt a pseudo-likelihood approximation and optimise the resulting penalised objective using the alternating direction method of multipliers (ADMM). We prove that the solution obtained by ADMM is equivalent to the optimal solution of the original objective function. Monte Carlo simulations demonstrate that FL-ERGM outperforms existing methods in estimating time-varying ERGM parameters and detecting structural changes. Applications to international trade and political co-voting networks reveal interpretable and contextually relevant patterns of temporal parameter variation.
Temporal networks capture systems in which interactions evolve over time. In transportation networks, this temporal structure governs the propagation of congestion and determines how junctions influence network-wide traffic flow. Existing temporal centrality measures primarily rely on temporal reachability or shortest paths, treating all feasible time-respecting paths equivalently once temporal ordering is satisfied while overlooking differences in travel delay, congestion and structural efficiency. To address this limitation, we propose Temporal Path-Based Centrality (TPBC), a centrality measure that evaluates node importance using three components: Relative Temporal Delay (RTD), Congestion Delay (CD) and Relative Hop (RH). Rather than relying on a single optimal path, TPBC aggregates contributions from all feasible time-respecting paths, weighting each according to its delay, congestion and structural efficiency. GPS trajectories from the T-Drive dataset are aligned to the road network using Hidden Markov Model (HMM)-based map matching to construct an event-based temporal network. To ensure computational tractability, path enumeration is bounded using practical limits on path length and the number of feasible paths. Experiments on the Chaoyang district road network across seven days demonstrate that TPBC exhibits the strongest correlation with observed traffic volume while capturing aspects of node importance not reflected by purely structural or instantaneous road-condition measures. TPBC identifies influential junctions predominantly on residential and lower-order roads rather than major arterial infrastructure. Node-removal experiments show that TPBC-selected junctions produce substantially greater reductions in time-respecting connectivity and network efficiency than random removal across all seven days. Sensitivity analyses confirm that these findings remain robust to variations in both the path-enumeration bounds and the relative weighting of delay, congestion and structural efficiency. These results establish TPBC as a robust, data-driven framework for identifying critical nodes in temporal transportation networks, with applications in traffic management, infrastructure planning and urban resilience.
While traditional real estate recommendation systems rely on objective metrics, they frequently fail to capture the complex, often irrational psychological landscapes and cognitive drivers governing high-stakes investment decisions. This study proposes an innovative framework based on the DIKWP (Data – Information – Knowledge – Wisdom – Purpose) process to comprehensively analyze user behaviors and latent psychological constructs in the context of real estate investment advisory. Beyond objective financial capacity and property attributes, the framework explicitly incorporates behavioral heuristics and psychological factors, such as cognitive biases, risk perception, and emotional decision-making tendencies, which play a critical role in real estate purchasing behavior. The proposed approach integrates customer profiles with heterogeneous real estate market data to quantify and model both observable actions and the underlying psychological drivers influencing investment intentions. Addressing key challenges in personalized recommendation and property selection, a decision-support system is designed and implemented to align real estate suggestions with users’ unique psychological profiles and cognitive characteristics. Experimental results demonstrate the effectiveness of the proposed system in delivering highly relevant and psychologically resonant recommendations, thereby enhancing decision confidence and supporting informed investment choices in a competitive real estate environment.
This study investigates how globalization has reshaped the world academic migration network. Using affiliation changes across almost 50 million Scopus articles, I construct five-decade-aggregated networks covering the period from 1974 to 2023. Three findings stand out. The network densifies and grows more equal: more country pairs are connected in each successive decade, and the share of flows captured by the largest hubs falls steadily, even as the United States retains the central position throughout. Reciprocity stays high at every threshold and in every decade, so academic migration is circulatory in structure rather than a one-directional drain from periphery to core. The rise of Asia takes the form of integration of China, India, and South Korea into the US-centered core, not a separate Asian cluster.
This study proposes a reduced-form, access-based model of network growth in which attachment depends jointly on accumulated connectivity, residual capacity, and origin–destination pair-specific link cost. The kernel contains Barabási–Albert (BA) preferential attachment, spatial attachment, and capacity-limited growth as limiting cases. At N=1000, α=1, and κ=2, the capacity-only specification ( β=1 , K=16 ) reduces mean maximum degree from 82.31 in the BA limiting case to 16.00, while the spatial-only specification ( ϕ=1 ) reduces mean edge length from 0.521 to 0.351 . Increasing ϕ from 0 to 2 shortens mean edge length from 0.526 to 0.157, and increasing κ from 1 to 4 raises clustering and reduces average path length. With mean capacity near 16 , integer-uniform and rounded-lognormal capacities raise mean maximum degree to 23.06 and 49.00 , respectively. Crossing-handling and access-guided extensions represent junction formation and compare four configured transport-growth strategies using a common ex post access measure. The framework yields directional hypotheses for empirical calibration.
