Financial stock returns correlations have been studied in the prism of random matrix theory to distinguish the signal from the “noise”. Eigenvalues of the matrix that are above the rescaled Marchenko–Pastur distribution can be interpreted as collective modes behavior while the modes under are usually considered as noise. In this analysis, we use complex network analysis to simulate the “noise” and the “market” component of the return correlations, by introducing some meaningful correlations in simulated geometric Brownian motion for the stocks. We find that the returns correlation matrix is dominated by stocks with high eigenvector centrality and clustering found in the network. We then use simulated “market” random walks to build an optimal portfolio and find that the overall return performs better than using the historical mean-variance data, up to [Formula: see text] on short-time scale.

This paper explores the dynamics of an eco-epidemic predator–prey model involving one prey and two competitive predator populations, with infection present in the predator population. The model uses a Holling-type II response function and includes a constant and linear proportion of prey refuge for susceptible and infected predators, respectively. It also accounts the effect of fear of predation and competition among predators for food and shelter. The study formulates the model system, identifies the steady-state points, and analyzes both local and global stability to understand the system’s long-term behavior. A formula for the basic reproduction number is constructed, indicating that controlling this number to be less than 1 can lead to disease eradication. Additionally, Hopf bifurcation in relation to key biological parameters is illustrated. Numerical simulations are conducted to validate the model, revealing diverse dynamic behaviors such as chaos and period-doubling with slight parameter variations.

The Riviera model is a combinatorial model for a settlement along a coastline, introduced recently by the authors. Of most interest are the so-called jammed states, where no more houses can be built without violating the condition that every house needs to have free space to at least one of its sides. In this paper, we introduce new agents (predators and altruists) that want to build houses once the settlement is already in the jammed state. Their behavior is governed by a different set of rules, and this allows them to build new houses even though the settlement is jammed. Our main focus is to detect jammed configurations that are resistant to predators, to altruists, and to both predators and altruists. We provide bivariate generating functions, and complexity functions (configurational entropies) for such jammed configurations. We also discuss this problem in the two-dimensional setting of a combinatorial settlement planning model that was also recently introduced by the authors, and of which the Riviera model is just a special case.

The limited availability of useful ground-truth communities in real-world networks presents a challenge to evaluating and selecting a “best” community detection method for a given network or family of networks. The use of comparable synthetic networks with planted ground-truths is one way to address this challenge. While several synthetic network generators can be used for this purpose, Stochastic Block Models (SBMs), when provided input parameters from real-world networks and clusterings, are well suited to producing networks that retain the properties of the network they are intended to model. We report, however, that SBMs can produce disconnected ground truth clusters; even under conditions where the input clusters are connected. In this study, we describe the REalistic Cluster Connectivity Simulator (RECCS), which, while retaining approximately the same quality for other network and cluster parameters, creates an SBM synthetic network and then modifies it to ensure an improved fit to cluster connectivity. We report results using parameters obtained from clustered real-world networks ranging up to 13.9 million nodes in size, and demonstrate an improvement over the unmodified use of SBMs for network generation.

Evolutionary graph theory explores how population structure influences evolutionary dynamics. This paper examines the impact of a simple subdivided population structure on mutant fixation probability and time under the Moran Birth–death process with constant fitness. We model the population as two fully connected subpopulations (cliques) connected by a few links. Using an analytical Markov-chain approach complemented by Monte Carlo simulations, we investigate how the size of the initial clique, where the mutant first appears, affects its eventual fixation outcomes. Our results demonstrate that initiating the process in a bigger clique enhances fixation probability and acts as an amplifier of selection compared to a well-mixed population, while a smaller starting clique suppresses selection. Furthermore, we observe that for small cliques, increased fitness reduces unconditional fixation time, whereas for larger cliques, it prolongs it. Conditional fixation time increases with starting clique size until reaching a critical threshold, after which it decreases. This critical size varies depending on the fitness value. Overall, we find that the combination of fitness level and starting clique size plays a crucial role in maximizing conditional fixation time.

This work proposes a hierarchical clustering algorithm for high-dimensional datasets using the cyclic space of reversible finite cellular automata. In cellular automaton (CA)-based clustering, if two objects belong to the same cycle, they are closely related and considered as part of the same cluster. However, if a high-dimensional dataset is clustered using the cycles of one CA, closely related objects may belong to different cycles. This paper identifies the relationship between objects in two different cycles based on the median of all elements in each cycle so that they can be grouped in the next stage. Further, to minimize the number of intermediate clusters which in turn reduces the computational cost, a rule selection strategy is taken to find the best rules based on information propagation and cycle structure. After encoding the dataset using frequency-based encoding such that the consecutive data elements maintain a minimum Hamming distance in encoded form, our proposed clustering algorithm iterates over three stages to finally cluster the data elements into the desired number of clusters given by user. When verified over standard benchmark datasets with various performance metrics, our algorithm is at par with the existing algorithms with quadratic time complexity.

Diffusion models drive the simulation for computing the expected spread of seed nodes, thereby helping in assessing the goodness of the chosen seeds. Without the inclusion of a model for simulating diffusion, the process of influence maximization (IM) remains incomplete. Given the co-existence of positive and negative relationship in real-world social networks, it becomes necessary to account for negative influences, as they can reverse the direction of influence diffusion. Additionally, the strength of relationships varies among social actors, with some links being strong and others weak. This variability in relationship strength significantly influences a node’s probability of sharing information with its neighboring nodes. Inspired by these actualities, we propose a novel Edge Sign and Strength-based Influence Diffusion (ESS-ID) model for influence diffusion in signed-weighted social networks. It calculates the likelihood of influence propagation from one node to another based on the weights of the edges. The sign associated with an edge is utilized to compute the actual positive influence exerted by a node. The performance of ESS-ID model is compared with three prevalent models used for studying diffusion in signed social networks, with emphasis not only the attainment of a large influence spread but also the achievement of a substantial positive influence spread. The comparative study was performed on 3 real-world signed social networks, and the obtained results establish that proposed ESS-ID model outperforms the other models by huge margins. ESS-ID is found to have attained an influence spread of approximately 85–90%, which is much higher compared to the other widely used models.

An abundance of literature has shown that the injection of noise into complex socio-economic systems can improve their resilience. This study aims to understand whether the same applies in the context of information diffusion in social networks. Specifically, we aim to understand whether the injection of noise in a social network of agents seeking to uncover a ground truth among a set of competing hypotheses can build resilience against disinformation. We implement two different stylized policies to inject noise in a social network, i.e. via random bots and via randomized recommendations, and find both to improve the population’s overall belief in the ground truth. Notably, we find noise to be as effective as debunking when disinformation is particularly strong. On the other hand, such beneficial effects may lead to a misalignment between the agents’ privately held and publicly stated beliefs, a phenomenon which is reminiscent of cognitive dissonance.

Decision-making processes often involve voting. Human interactions with exogenous entities such as legislations or products can be effectively modeled as two-mode (bipartite) signed networks — where people can either vote positively, negatively, or abstain from voting on the entities. Detecting communities in such networks could help us understand underlying properties: for example ideological camps or consumer preferences. While community detection is an established practice separately for bipartite and signed networks, it remains largely unexplored in the case of bipartite signed networks. In this paper, we systematically evaluate the efficacy of community detection methods on projected bipartite signed networks using a synthetic benchmark and real-world datasets. Our findings reveal that when no communities are present in the data, these methods often recover spurious user communities. When communities are present, the algorithms exhibit promising performance, although their performance is highly susceptible to parameter choice. This indicates that researchers using community detection methods in the context of bipartite signed networks should not take the communities found at face value: it is essential to assess the robustness of parameter choices or perform domain-specific external validation.

The issue of network community detection has been extensively studied across many fields. Most community detection methods assume that nodes belong to only one community. However, in many cases, nodes can belong to multiple communities simultaneously. This paper presents two overlapping network community detection algorithms that build on the two-step approach, using the extended modularity and cosine function. The applicability of our algorithms extends to both undirected and directed graph structures. To demonstrate the feasibility and effectiveness of these algorithms, we conducted experiments using real data.