Other applications of Complex Systems

Though an unclear problem, community detection is a popular research topic. One example is the use of statistical inference to classify nodes into communities. Typically, the community assignments are passed as a message through a noisy channel and then decoded once received. Radicchi [1], instead, used a stochastic model to add noise that introduced community structure into the graph. He also produced an iterative quadratic time algorithm for decoding, based on the Gallagher decoder, and tested it on graphs produced by the stochastic block model.

The massive adoption of social network technologies makes it urgent to develop a deeper understanding of how they affect the formation of opinions and ideologies. Ideas are formed through the interaction with social neighbours, and it is known that the social network topology plays an important role in this process. Moreover, opinions are not formed in isolation but are affected by the evolution of related ideas, and the complexity and feedback introduced by this co-evolution introduces effects that have not been studied in detail.

Ranking is a ubiquitous phenomenon in human society. On the web
pages of Forbes, one may find all kinds of rankings, such as the world’s most
powerful people, the world’s richest people, the highest-earning tennis players,
and so on and so forth. Herewith, we study a specific kind—sports ranking
systems in which players’ scores and/or prize money are accrued based on their
performances in different matches. By investigating 40 data samples which span
12 different sports, we find that the distributions of scores and/or prize money

How does the small-scale topological structure of an airline network behave as the network evolves? To address this question, we study the dynamic and spatial properties of small undirected subgraphs using 15 years of data on Southwest Airlines' direct route service on the U.S. domestic market. We find that this real-world network has much in common with random graphs, and describe a possible power-law scaling between subgraph counts and the number of edges in the network, that appears to be quite robust to changes in network density and size.

Link-prediction is an active research field within network theory, aiming at uncovering missing connections or predicting the emergence of future relationships from the observed network structure. Here we propose a novel method for link-prediction that relies on the entropy maximization procedure introduced in the framework of network reconstruction. Given set of constraints (e.g.

We propose to relate the social structure of towns and cities to the presence of corruption in their local governments using data from Hungary. We measure corruption risk using data on public contract awards and social structure using data from iWiW[1], a defunct online social network. We find that towns with a fragmented social structure have significantly more corruption risk in their contracts, while towns made up of people with more diverse networks have significantly less corruption risk.

Social media analysis is an increasingly popular field, due to the vast amounts of real data. Here, we use sentiment in order to find patterns surrounding real-world events, specifically patterns involving the underlying behavior in populations. Using VADER, a lexicon based sentiment analysis tool, and OSOME, a platform for collecting data from Twitter, we analyze tweets surrounding major events, such as social unrest and emergency disasters.

It has been observed that large international scientific collaborations have been steadily grown in the recent decades; however, the mechanisms and dynamics behind are less well understood. This study examines spatial-temporal structures of scientific collaboration networks from the Microsoft Academic Graphs (MAG) from 1960 to 2017. We quantified the level of international collaborations in research papers and the influence of geographic distance and socioeconomic factors on collaborations.

We consider a simple model of interacting agents, each holding an opinion about herself and the others. This model is the opinion propagation submodel of [1]. During random encounters by pairs, agents modify their opinions under the noisy influence of others. The influence is attractive and agents’ opinions are more strongly attracted by the opinions of whom they value higher than themselves and vice versa.

Rich-club ordering refers to tendency of nodes with a high degree to be more interconnected than expected. Such kind of ordering can be quantitatively recognized via the coefficient φ(k)=(2E_(>k))/(N_(>k) (N_(>k)-1)) where E_(>k) is the number of links among the N_(>k) nodes having degree higher than a given value k and (N_(>k) (N_(>k)-1))/2 is the maximum possible number of links among the N_(>k) nodes [1].