Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:FUZHONG NIAN, JIANJIAN ZHOU, YINUO QIAN Abstract: Advances in Complex Systems, Ahead of Print. The interaction of information and the evolution of network structure are inseparable. In order to construct social network evolution and information propagation models that better fit real-world scenarios, this paper proposes a social network structure evolution model driven by changes in the strength of relationships between individuals through their information interactions with each other. During the evolution process of the network, information interaction between individuals is also influenced by the network structure. Therefore, we improve traditional propagation models and construct an information propagation model with dynamic propagation rates. The proposed model is used to simulate both the spread of information and the evolution of network structures in real social networks. Finally, simulation results are compared to real-world data, demonstrating the effectiveness and rationality of the proposed model. Citation: Advances in Complex Systems PubDate: 2023-11-28T08:00:00Z DOI: 10.1142/S0219525923500108
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:JUNWEI SU, PETER MARBACH Abstract: Advances in Complex Systems, Ahead of Print. Empirical studies have consistently demonstrated the presence of a core–periphery structure within social network communities. Nevertheless, a formal model and comprehensive analysis to fully understand the structural characteristics of these communities are still lacking. This paper seeks to characterize these properties, focusing on agents’ interconnections and their allocation of rates. Employing a game-theoretic approach, our analysis unveils several novel insights. First, we show that periphery agents not only follow core agents but also other periphery agents who share similar primary interests. Second, our results illuminate the emergence of core–periphery communities, revealing the conditions under which they form, and how they form. Citation: Advances in Complex Systems PubDate: 2023-11-22T08:00:00Z DOI: 10.1142/S0219525923400040
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:HIROSHI HAMADA Abstract: Advances in Complex Systems, Ahead of Print. This paper analyzes the Talent versus Luck model, which examines the impact of talent and luck on an individual’s career success. The original simulation-based model demonstrated that the distribution of capital has a heavy tail, and the most successful individuals are not necessarily the most talented. While the implications of the original model are intriguing, those findings were based solely on numerical calculations, and it was unclear how generally valid they are. Challet et al. generalize the original model using an analytical approach and successfully clarify the relationship between talent, lucky events, and capital when talent is constant and follows a uniform distribution. We reformulate a simplified model and derive more general propositions about the relationship between luck and talent in individual success by introducing the new concept of luck of outcome in addition to the luck of opportunity in previous models. We show that the capital distribution generated from a simplified talent versus luck model follows a lognormal distribution even when the talent is subject to a normal distribution. Moreover, we specify the relationship between the inequality of the distribution, which is indicated by the Gini coefficient, and the parameters of talent distribution. Citation: Advances in Complex Systems PubDate: 2023-11-18T08:00:00Z DOI: 10.1142/S021952592350008X
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:CARLOS ZAPATA-CARRATALÁ, MAXIMILIAN SCHICH, TALIESIN BEYNON, XERXES D. ARSIWALLA Abstract: Advances in Complex Systems, Ahead of Print. Theoretical and computational frameworks of complexity science are dominated by binary structures. This binary bias, seen in the ubiquity of pair-wise networks and formal binary operations in mathematical models, limits our capacity to faithfully capture irreducible polyadic interactions in higher-order systems. A paradigmatic example of a higher-order interaction is the Borromean link of three interlocking rings. In this paper, we propose a mathematical framework via hypergraphs and hypermatrix algebras that allows to formalize such forms of higher-order bonding and connectivity in a parsimonious way. Our framework builds on and extends current techniques in higher-order networks — still mostly rooted in binary structures such as adjacency matrices — and incorporates recent developments in higher-arity structures to articulate the compositional behavior of adjacency hypermatrices. Irreducible higher-order interactions turn out to be a widespread occurrence across natural sciences and socio-cultural knowledge representation. We demonstrate this by reviewing recent results in computer science, physics, chemistry, biology, ecology, social science, and cultural analysis through the conceptual lens of irreducible higher-order interactions. We further speculate that the general phenomenon of emergence in complex systems may be characterized by spatio-temporal discrepancies of interaction arity. Citation: Advances in Complex Systems PubDate: 2023-11-09T08:00:00Z DOI: 10.1142/S0219525923500078
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:EMILY CHAO-HUI HUANG, FREDERICK KIN HING PHOA Abstract: Advances in Complex Systems, Ahead of Print. An ego-centric network consists of a particular node (ego) that has relationships to all neighboring nodes (alters) in the network. Such network serves as an important tool to study the network structure of alters of the ego, and it is essential to present such network with good visualization. This work aims at introducing an efficient method, namely the Uniform Placement of Alters on Spherical Surface (U-PASS), to represent an ego-centric network so that all alters are scattered on the surface of the unit sphere uniformly. Unlike other simple uniformity that considers to maximize Euclidean distances among nodes, U-PASS is a three-stage method that spreads the alters with the consideration of existing edges among alters, no overlapping of node clusters, and node attribute information. Particle swarm optimization is employed to improve efficiency in node allocations. To guarantee the uniformity, we show the connection between our U-PASS to the minimum energy design on a two-dimensional flat plane with a specific gradient. Our simulation study shows good performance of U-PASS in terms of some distance statistics when compared to four state-of-the-art methods via self-organizing maps and force-driven approaches. We use a Facebook network to illustrate how this ego-centric network looks different after the alter nodes are allocated via our U-PASS. Citation: Advances in Complex Systems PubDate: 2023-10-31T07:00:00Z DOI: 10.1142/S0219525923400039
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:NIKOLAOS NAKIS, ABDULKADIR ÇELIKKANAT, MORTEN MØRUP Abstract: Advances in Complex Systems, Ahead of Print. Graph representation learning (GRL) has become a prominent tool for furthering the understanding of complex networks providing tools for network embedding, link prediction, and node classification. In this paper, we propose the Hybrid Membership-Latent Distance Model (HM-LDM) by exploring how a Latent Distance Model (LDM) can be constrained to a latent simplex. By controlling the edge lengths of the corners of the simplex, the volume of the latent space can be systematically controlled. Thereby communities are revealed as the space becomes more constrained, with hard memberships being recovered as the simplex volume goes to zero. We further explore a recent likelihood formulation for signed networks utilizing the Skellam distribution to account for signed weighted networks and extend the HM-LDM to the signed Hybrid Membership-Latent Distance Model (sHM-LDM). Importantly, the induced likelihood function explicitly attracts nodes with positive links and deters nodes having negative interactions. We demonstrate the utility of HM-LDM and sHM-LDM on several real networks. We find that the procedures successfully identify prominent distinct structures, as well as how nodes relate to the extracted aspects providing favorable performances in terms of link prediction when compared to prominent baselines. Furthermore, the learned soft memberships enable easily interpretable network visualizations highlighting distinct patterns. Citation: Advances in Complex Systems PubDate: 2023-09-27T07:00:00Z DOI: 10.1142/S0219525923400027
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:XUEYING LIU, ZHIHAO HU, XINWEI DENG, CHRIS J. KUHLMAN Abstract: Advances in Complex Systems, Ahead of Print. When modeling human behavior in multi-player games, it is important to understand heterogeneous aspects of player behaviors. By leveraging experimental data and agent-based simulations, various data-driven modeling methods can be applied. This provides a great opportunity to quantify and visualize the uncertainty associated with these methods, allowing for a more comprehensive understanding of the individual and collective behaviors among players. For networked anagram games, player behaviors can be heterogeneous in terms of the number of words formed and the amount of cooperation among networked neighbors. Based on game data, these games can be modeled as discrete dynamical systems characterized by probabilistic state transitions. In this work, we present both Frequentist and Bayesian approaches for visualizing uncertainty in networked anagram games. These approaches help to elaborate how players individually and collectively form words by sharing letters with their neighbors in a network. Both approaches provide valuable insights into inferring the worst, average, and best player performance within and between behavioral clusters. Moreover, interesting contrasts between the Frequentist and Bayesian approaches can be observed. The knowledge and inferences gained from these approaches are incorporated into an agent-based simulation framework to further demonstrate model uncertainty and players’ heterogeneous behaviors. Citation: Advances in Complex Systems PubDate: 2023-08-24T07:00:00Z DOI: 10.1142/S0219525923400015
Hybrid journal (It can contain Open Access articles)
