Identifying structurally prominent users and selecting effective seed nodes are central problems in social network analysis and influence maximization. This study analyzes a directed Twitter mention network constructed from approximately 1.6 million tweets, comprising 552,121 users and 641,497 directed edges. We characterize user positions using PageRank, in-degree, and out-degree centrality and compare PageRank-based and out-degree-based seed selection under the Independent Cascade (IC) model. The network exhibits highly skewed structural centrality patterns. Seed-set performance is evaluated through 1,000 independent Monte Carlo simulations with a uniform propagation probability of p=0.1 . We report mean spread, standard deviation, 95
This study conducts a comparative time-series analysis of logistics-themed YouTube content from Japan and South Korea, examining how logistics issues are perceived and discussed in both nations. Amidst global supply chain disruptions, the process of forming societal perceptions remains under-researched. By analyzing video subtitles as “sender discourse” and viewer comments as “receiver reactions,” this study utilizes a hybrid methodology of word frequency and co-occurrence network analysis. The findings reveal that the framing and emphasis of logistics problems vary significantly by country. Furthermore, a distinct divergence exists between the frames presented in subtitles and the reactions in comments. These results suggest that logistics issues are not merely technical or economic challenges; rather, they are perceived through lenses shaped by each country’s specific social context and media environment. This research highlights the critical role of digital platforms in reflecting and constructing localized public discourse on logistics.
Source identification in propagation graphs is an inverse problem that aims to recover the initial infected or activated nodes from an observed propagation snapshot. This paper studies a static, structure-driven setting in which temporal traces, observer timestamps, and large labeled diffusion histories are unavailable. Voting and ranking-fusion methods are often motivated by the ensemble-learning intuition that several imperfect signals can be more useful after aggregation than any single signal alone. The study tests whether this intuition also helps multi-source localization on propagation graphs, where the individual signals are structural node heuristics rather than learned classifiers. The evaluation covers ten structural heuristics, four ensemble aggregation families, and four implemented source-localization baselines across 22 network-by-propagation cases and 198 saved propagation instances. The analysis separates fixed-method comparisons from diagnostic headroom, compares structural ensembles with non-structural source-localization baselines, and examines whether source counts can be estimated from score gaps or score thresholds rather than supplied as an oracle k. The results support a qualified conclusion. Structural ensembles expose a statistically reliable direction of improvement over selected single-heuristic references, but practical fixed-method gains are modest and depend on how member weights or configurations are chosen. Average-distance and topological centrality are the strongest recurring structural signals, suggesting that future methods should model proximity and graph-position evidence more explicitly rather than enumerate more voting combinations. Adaptive epsilon-gap and threshold rules partially control source-count error, but they do not remove the source-count problem under static snapshots. Overall, ensemble ranking fusion is useful as a calibrated diagnostic and sometimes as a practical aggregation layer, while richer temporal information, validation data, or more principled source-count estimators are still needed for robust deployment.
Information propagation in Online Social Networks (OSNs) is commonly modeled as a diffusion process based on fixed interaction rules. However, such models are limited in their ability to capture adaptive and context-dependent interactions. In this work, we propose an attention-based formulation of information propagation in which influence emerges from dynamic interactions between node representations. Rather than following predefined transmission rules, propagation is governed by compatibility between node states and evolves over time. Experiments on synthetic networks show that the proposed model exhibits non-monotonic activation dynamics, in contrast to classical cascade processes. A key result is the emergence of distinct propagation regimes controlled by a structural parameter, which governs a transition from globally distributed, high-entropy interactions to localized, low-entropy dynamics. These findings indicate that propagation can be understood as a process of dynamic attention redistribution rather than cumulative transmission. As a result, the proposed approach provides a unified and flexible approach to modeling information propagation, generalizing classical diffusion models and capturing a broader range of behaviors.
Influencer-centered communities on social media are connected through overlapping audiences and shared topical attention, yet their directed sentiment dependencies remain difficult to observe directly. We reconstruct inter-community influence networks from sentiment time series on Weibo, a major Chinese microblogging platform. We track several prominent technology bloggers over six months, aggregating comment-level sentiment into collective sentiment trajectories for each blogger’s commenting audience. These audiences are treated as macro-agents, an aggregation justified by shared content exposure and homophily-driven sentiment alignment. Using opinion dynamics models as network inference tools, we estimate directed influence graphs capturing how collective sentiment in one community shapes that in others. The inferred networks exhibit pronounced sparsity and clear structural asymmetry. Some communities act as hubs whose sentiment radiates outward, while others are largely internally driven, reacting to their blogger’s posts and weakly coupled to peer communities. These patterns remain consistent across model variants, and the learned models demonstrate stable two-period-ahead predictive performance on held-out data. Our findings show that collective online sentiment is well described by structured inter-community averaging, yielding interpretable, empirically grounded influence graphs. The proposed framework provides an interpretable approach for mapping inter-community sentiment dependencies from social media time series.
