The broad degree distributions Q k are indicative of the fact that the number of actors engaging with very large number of actors are also significant. Physically this means that the actors constituting one cluster have never been involved with any actor from a different cluster. This tells us that the fraction of nodes in the largest cluster grows with the total size of the network quite fast so that eventually the fraction of nodes outside the largest cluster will be negligible.
We computed the CCDF Q s of cluster size s and find that it roughly has a power law decay for components except the largest one, with a decay exponent roughly close to 3 see Fig. Plot of the cumulative probability CCDF Q s that there is a cluster of size larger than s , for data aggregated over the period — The size of the largest cluster is very large compared to the rest.
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In order to explore the dynamics of the process that leads to the broad distribution of the mentions and degrees, we investigate the dynamics of growth for these quantities over the span of 15 years — For good statistics, we ranked the actors according to the number of mentions and degrees, and analyzed the data for the top 10 actors. S6 for degree. We thus identify that the growth rate of the system network is not independent of the size of the node degree or mentions.
We also study how the network breaks down under attack, in order to investigate the possibility of preventing unruly events to spread The largest connected component of the network is subjected to targeted attack by removal of the most connected nodes. We start by removing the node with the highest degree, followed by the next highest and so on. This results in rapid fragmentation or destruction of the network. We compute the fraction of nodes G present in the largest cluster, which is observed to decrease very quickly Fig.
This exercise indicates that targeted intervention may help stop spreading of ethnic conflicts and human rights violations. The structure of the network under attack: Nodes are removed in the sequence of their degrees starting from the highest degree. The structure of the network under random failure: Nodes are removed randomly. Results are shown for c EC and d HR networks.
The networks are destroyed very quickly by targeted node removal attack , compared to random node removal failure. The results are for networks aggregated over — Considering that we have time series data for the number of mentions of ethnic conflicts and human rights violations from news articles it would be interesting to study if there is a clear cause and effect relation between these events with time.
To determine the causal effect purely from the observations of the past data, we apply Granger Causality 21 which estimates the causal relationship by observing the changes in the distribution of the variables over time.
Let us consider two random variables depicting counts of EC and HR. To say that EC causes HR, Granger causality computes a regression of variable HR on the past values of itself and the past values of EC and then tests the significance of coefficient estimates associated with EC.
We set up a null hypothesis and to test the significance of the coefficients, compute the p value. If the p -value is less than 0. We tested the counts for year wise as well as month wise mentions. Hence, we can confirm that ethnic conflicts cause human rights violations, while human rights violations are not responsible for ethnic conflicts.
The details of the analysis are provided in the Supplementary Information. News reports serve as a reasonable proxy for the importance and intensity of events. The intensity is reflected by the number of reports and lingering span of time through which the reports follow. Our study focuses on events that pointed at ethnic conflicts and human rights violations. The US government had recently funded in a large-scale project, the Integrated Conflict Early Warning System ICEWS 22 , which makes use of quantitative data and statistical methods in order to forecast events of political instability, which include international and domestic crises, ethnic and religious violence, rebellion and insurgency.
The frequencies of mentions of actors and their co-mentions with others can be treated well as proxies for their importance and influence, as well as involvement with others. The aggregate data enables us to construct a network of actors, and even finding disconnected groups. In fact, our study reveals that most events are disconnected in very small clusters while very large clusters of frequently engaging actors exist.
One can study the geographical localization of events and clusters to procure detailed information regarding the context, intensity and growth pattern of events Identifying important groups of actors, in terms of their intensities of activities, is important for possible intervention that may prevent the spread of such events. The probability distributions of actor mentions, co-actor mentions and the degree of an actor have power law tails for the largest values, indicating a strong self-organizing principle behind the events.
The growth properties of individual actor nodes indicate that in the long run, very small fraction of disconnected clusters are left, while most of the actors belong to a giant connected component. The data on ethnic conflicts and human rights violations are found to be strikingly similar in terms of static and dynamic properties of the network, and even in terms of network stability against failure as well as targeted attack.
This may point at a very high degree of correlation between events. In fact, using a causality analysis, we could quantitatively conclude that ethnic conflicts lead to human rights violations, while the reverse may not be true. These networks of ethnic conflicts and human rights violations reflect the negative aspects of human behavior and cooperation, and are certainly different from other social networks friendship, collaboration, etc. This detailed scientific study of the network structure, dynamics, function and resilience may help policy makers, specifically in cases where there is a need for preventing the spread of ethnic tensions and conflicts.
There are possibilities of similar analyses using data from online social media, e.
We have deliberately avoided mentioning the real names and other details because the main aim of the paper was to study the network properties and statistical regularities. Certainly, it would be interesting to address such sociological explanations, implications and policies in the future.
Specific studies on the geographical implications of actor networks, the geographical and socio-cultural influence of their robustness under possible intervention will be important issues to study. Using Google BigQuery 26 , it is possible to extract data for each event, having an unique time stamp, and providing the data about news about ethnic conflicts EC and human rights violations HR happening around the world spanning over a large time scale.
The data contains information about a pair of actors involved, the location information of the event, as well as latitude, longitude data of actors and the event. We procured 45, events for EC and 48, for HR for a 15 year period, — We filtered out those data for which both actors were mentioned, along with their respective location information. We have filtered out and excluded those data rows, which have at least one entry missing corresponding to the attributes we were interested in. Hence, after cleaning, we analyzed 28, events of EC and 36, of HR.
If another event E 2 within the same time window involves actors A 2 and A 3 , then A 3 is connected to A 2 with a link of unit weight. Thus A 1 and A 3 are both connected to A 2 see Fig. Aggregating all such events over the time window T , connected components emerge, with link weights increasing if the same pair of actors linking them appear in multiple events actor pair mentions. These connected components form a complex network of nodes actors and links actor pair mentions.
Sehgal and B. Sehgal, B. All authors reviewed the manuscript. Electronic supplementary material. Supplementary information accompanies this paper at doi Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. National Center for Biotechnology Information , U. Sci Rep. Published online Aug Author information Article notes Copyright and License information Disclaimer. Arnab Chatterjee, Email: moc. Justify Text. Note: preferences and languages are saved separately in https mode.
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