Human Papillomavirus infection is the most common sexually transmitted infection, and can cause serious complications such as cervical cancer. Due to the scarcity of empirical data about sexual relationships in varying demographics, computationally modelling the underlying sexual contact networks is important to understand Human Papillomavirus infection dynamics. Here we present SeCoNet, a heterosexual contact network growth model for Human Papillomavirus disease simulation. The growth model consists of three mechanisms that closely imitate real-world relationship forming and discontinuation processes in sexual contact networks. We also undertake disease dynamics analysis of Human Papillomavirusus using this model.

We employ an agent-based contact network model to study the relationship between vaccine uptake and disease dynamics in a hypothetical country town from New South Wales, Australia, undergoing a COVID-19 epidemic, over a period of three years. We model the contact network in this hypothetical township of N = 10000 people as a scale-free network, and simulate the spread of COVID-19 and vaccination program using disease and vaccination uptake parameters typically observed in such a NSW town. We simulate the spread of the ancestral variant of COVID-19 in this town, and study the disease dynamics while the town maintains limited but non-negligible contact with the rest of the country which is assumed to be undergoing a severe COVID-19 epidemic. We also simulate a maximum three doses of Pfizer Comirnaty vaccine being administered in this town, with limited vaccine supply at first which gradually increases, and analyse how the vaccination uptake affects the disease dynamics in this town, which is captured using an extended compartmental model with epidemic parameters typical for a COVID-19 epidemic in Australia. Our results show that, in such a township, three vaccination doses are sufficient to contain but not eradicate COVID-19, and the disease essentially becomes endemic. We also show that the average degree of infected nodes (the average number of contacts for infected people) predicts the proportion of infected people. Therefore, if the hubs (people with a relatively high number of contacts) are disproportionately infected, this indicates an oncoming peak of the infection, though the lag time thereof depends on the maximum number of vaccines administered to the populace. Overall, our analysis provides interesting insights in understanding the interplay between network topology, vaccination levels, and COVID-19 disease dynamics in a typical remote NSW country town.

Social systems are increasingly being modelled as complex networks, and the interactions and decision making of individuals in such systems can be modelled using game theory. Therefore, networked game theory can be effectively used to model social dynamics. Individuals can use pure or mixed strategies in their decision making, and recent research has shown that there is a connection between the topological placement of an individual within a social network and the best strategy they can choose to maximise their returns. Therefore, if certain individuals have a preference to employ a certain strategy, they can be swapped or moved around within the social network to more desirable topological locations where their chosen strategies will be more effective. To this end, it has been shown that to increase the overall public good, the cooperators should be placed at the hubs, and the defectors should be placed at the peripheral nodes. In this paper, we tackle a related question, which is the time (or number of swaps) it takes for individuals who are randomly placed within the network to move to optimal topological locations which ensure that the public utility satisfies a certain utility threshold. We show that this time depends on the topology of the social network, and we analyse this topological dependence in terms of topological metrics such as scale-free exponent, assortativity, clustering coefficient, and Shannon information content. We show that the higher the scale-free exponent, the quicker the public utility threshold can be reached by swapping individuals from an initial random allocation. On the other hand, we find that assortativity has negative correlation with the time it takes to reach the public utility threshold. We find also that in terms of the correlation between information content and the time it takes to reach a public utility threshold from a random initial assignment, there is a bifurcation: one class of networks show a positive correlation, while another shows a negative correlation. Our results highlight that by designing networks with appropriate topological properties, one can minimise the need for the movement of individuals within a network before a certain public good threshold is achieved. This result has obvious implications for defence strategies in particular.

The maximum matching problem has been extensively studied, and several algorithms have been proposed which can maximize the percentage of matching. Nevertheless, these algorithms are designed without consideration of the topology of the networks on which they are intended to be applied. However, recent research has shown that many distributed systems from social, technical and biological domains which can be represented as networks display the scale-free structure, which has well-known topological characteristics such as the power-law degree distribution. In this paper, we describe a simple iterative heuristic for maximum matchings in scale-free networks which takes advantage of such characteristics. We show that our heuristic is no worse than the best known version of the Blossom algorithm in terms of time-complexity, and much better than any version of Blossom in terms of memory usage (space-complexity) when applied to typical scale-free networks. The heuristic due to its simplicity and memory efficiency is a viable alternative to Blossom in most real world applications.

Influence models enable the modelling of the spread of ideas, opinions and behaviours in social networks. Bounded rationality in social network suggests that players make non optimum decisions due to the limitations of access to information. Based on the premise that adopting a state or an idea can be regarded as being 'rational', we propose an influence model based on the heterogeneous bounded rationality of players in a social network. We employ the quantal response equilibrium model to incorporate the bounded rationality in the context of social influence. The bounded rationality of following a seed or adopting the strategy of a seed would be negatively proportional to the distance from that node. This indicates that the closeness centrality would be the appropriate measure to place influencers in a social network. We argue that this model can be used in scenarios where there are multiple types of influencers and varying payoffs of adopting a state. We compare different seed placement mechanisms to compare and contrast the optimum method to minimise the existing social influence in a network when there are multiple and conflicting seeds. We ascertain that placing of opposing seeds according to a measure derived from a combination of the betweenness centrality values from the seeds and the closeness centrality of the network would provide the maximum negative influence.

We study evolution of coordination in social systems by simulating a coordination game in an ensemble of scale-free and small-world networks and comparing the results. We give particular emphasis to the role information about the pay-offs of neighbours plays in nodes adapting strategies, by limiting this information up to various levels. We find that if nodes have no chance to evolutionarily adapt, then non-coordination is a better strategy, however when nodes adapt based on information of the neighbour payoffs, coordination quickly emerges as the better strategy. We find phase transitions in number of coordinators with respect to the relative pay-off of coordination, and these phase transitions are sharper in small-world networks. We also find that when pay-off information of neighbours is limited, small-world networks are able to better cope with this limitation than scale-free networks. We observe that provincial hubs are the quickest to evolutionarily adapt strategies, in both scale-free and small world networks. Our findings confirm that evolutionary tendencies of coordination heavily depend on network topology.

Mixing patterns in social networks can give us important clues about the structure and functionality of these networks. In the past, a number of measures including variants of assortativity have been used to quantify degree mixing patterns of networks. In this paper, we are interested in observing the heterogeneity of the neighbourhood of nodes in networks. For this purpose, we use the standard deviation of degree differences between a node and its neighbours. We call this measure the 'versatility' of a node. We apply this measure on synthetic and real world networks. We find that among real world networks three classes emerge -(i) Networks where the versatility converges to non-zero values with node degree (ii) Networks where the versatility converges to zero with node degree (iii) Networks where versatility does not converge with node degree. We find that there may be some correlation between this and network density, and the geographical / anatomical nature of networks may also be a factor. We also note that versatility could be applicable to any quantifiable network property, and not just node degree.

In this exploratory paper, we examine the evolution and engagement of an online community through a ten-year period. Data is collected from an online public discussion forum provided by a government-sponsored website specifically developed for community capacity building. We postulate that there are clear patterns of assortativity where similar actors engage in communication with each other over time. Results show that there is a clear pattern of networks losing their disassortative character in the early years followed by disassortative networks in the later years. The network-level results challenges government-level metrics of community-building success and suggests network analysis as an empirical avenue for understanding social processes involved in the very nature of community building.

In this paper, we introduce a measure to analyse the structural robustness of complex networks, which is specifically applicable in scenarios of targeted, sustained attacks. The measure is based on the changing size of the largest component as the network goes through disintegration. We argue that the measure can be used to quantify and compare the effectiveness of various attack strategies. Applying this measure, we confirm the result that scale-free networks are comparatively less vulnerable to random attacks and more vulnerable to targeted attacks. Then we analyse the robustness of a range of real world networks, and show that most real world networks are least robust to attacks based on betweenness of nodes. We also show that the robustness of some networks are more sensitive to the attack strategy compared to others, and given the disparity in the computational complexities of calculating various centrality measures, the robustness coefficient introduced can play a key role in choosing the attack and defence strategies for real world networks. While the measure is applicable to all types of complex networks, we clearly demonstrate its relevance to social network analysis.

Study of the dynamics of longitudinal networks has already attracted enormous research interest. Although dynamics of networks can be captured both at network-level and node / actor-level, the latter has gained less attention in current literature. By following a topological approach (i.e., static topology and dynamic topology) to analyze networks, this paper first proposes a research framework to capture actor-level dynamics for longitudinal networks. In static topology, Social Network Analysis (SNA) methods are applied on the aggregated network of entire data collection period. A smaller segment of network data that are accumulated in less time compared to the entire data collection period are used in dynamic typology for analysis purpose. This study further successfully compiles and applies this framework to the context of organizational crisis and project dynamics with the purpose to explore different level of actor-level dynamics at the different operational environment of these contexts over time. It is noticed that different level of actor-level dynamics are observed in the communication and collaboration network during the different facets of the organizations. In the context of organizational crisis, it is evident that during the 'crisis' period of operational running of organization, actors in the organizational email communication networks show higher level of actor-level dynamics compared to the 'normal' period. Less actor-level dynamics are observed during the 'final' phase of project life cycle, as found from the second context.