Social media enabled a direct path from producer to consumer of contents changing the way users get informed, debate, and shape their worldviews. Such a disintermediation might weaken consensus on social relevant issues in favor of rumors, mistrust, or conspiracy thinking—e.g., chem-trails inducing global warming, the link between vaccines and autism, or the New World Order conspiracy. Previous studies pointed out that consumers of conspiracy-like content are likely to aggregate in homophile
#sna by Andrie de Vries In a previous post, I used page rank and community structure to create a plot of CRAN. This plot used vibrant colours to allow us to see some of the underlying structure of CRAN. However, much of this structure was still obfuscated by the amount of detail. Concretely, a large number of dots (packages) made it difficult to easily see the community structure.
The critical node detection problem (CNDP) aims to fragment a graph G=(V,E) by removing a set of vertices R with cardinality |R|≤k, such that the residual graph has minimum pairwise connectivity for user-defined value k. Existing optimization algorithms are incapable of finding a good set R in graphs with many thousands or millions of vertices due to the associated computational cost. Hence, there exists a need for a time- and space-efficient approach for evaluating the impact of removing any v∈V in the context of the CNDP. In this paper, we propose an algorithm based on a modified depth-first search that requires O(k(|V|+|E|)) time complexity. We employ the method within in a greedy algorithm for quickly identifying R. Our experimental results consider small- (≤250 nodes) and medium-sized (≤25,000 nodes) networks, where it is possible to compare to known optimal solutions or results obtained by other heuristics. Additionally, we show results using six real-world networks. The proposed algorithm can be easily extended to vertex- and edge-weighted variants of the CNDP.
Social networks are often degree correlated between nearest neighbors, an effect termed homophily, wherein individuals connect to nearest neighbors of similar connectivity. Whether friendships or other associations are so correlated beyond the first-neighbors, and whether such correlations are an inherent property of the network or are dependent on other specifics of social interactions, remains unclear.
Here we address these problems by examining long-range degree correlations in three undirected online social and three undirected nonsocial (airport, transcriptional-regulatory) networks. Degree correlations were measured using Pearson correlation scores and by calculating the average neighbor degrees for nodes separated by up to 5 sequential links. We found that the online social networks exhibited primarily weak anticorrelation at the first-neighbor level, and tended more strongly towards disassortativity as separation distances increased. In contrast, the nonsocial networks were disassortative among first-neighbors, but exhibited assortativity at longer separation distances. In addition, the average degrees of the separated neighbors approached the average network connectivity after approximately 3-4 steps. Finally, we observed that two algorithms designed to grow networks on a node-by-node basis failed to reproduce all the correlative features representative of the social networks studied here.
Someone recently posted an issue on RNeo4j where they needed help visualizing their graph in visNetwork, which has turned out to be a pretty fun R package. I decided to turn my answer into a blog post.
In this paper, we review some advances made recently in the study of mobile phone datasets. This area of research has emerged a decade ago, with the increasing availability of large-scale anonymized datasets, and has grown into a stand-alone topic. We survey the contributions made so far on the social networks that can be constructed with such data, the study of personal mobility, geographical partitioning, urban planning, and help towards development as well as security and privacy issues.
Dealing with big data in computational social networks may require powerful machines, big storage, and high bandwidth, which may seem beyond the capacity of small labs. We demonstrate that researchers with limited resources may still be able to conduct big-data research by focusing on a specific type of data. In particular, we present a system called MPT (Microblog Processing Toolkit) for handling big volume of microblog posts with commodity computers, which can handle tens of millions of micro posts a day. MPT supports fast search on multiple keywords and returns statistical results. We describe in this paper the architecture of MPT for data collection and phrase search for returning search results with statistical analysis. We then present different indexing mechanisms and compare them on the microblog posts we collected from popular online social network sites in mainland China.
mathematical aspects, and applications of social computing
Focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media
By Douglas Ashton, Consultant One of the big successes of data analytics is the cultural change in how business decisions are being made. There is now wide spread acceptance of the role that data science has to play in decision making.
Social networks underly a broad range of social and economic research questions that are increasingly being understood through large-scale computational analyses. In particular, the study of social influence and information diffusion on social networks have rich modeling histories, while opportunities in online instrumentation and experimentations are now providing tremendous advances in our abilities for theory testing as well as theory development.
This tutorial will provide a brief overview of models of social networks and social influence, and then focus on giving an overview of recent evidence for how these processes behave empirically in diverse online settings. A particular emphasis will be placed on efforts to approach these problems through causal inference, moving beyond "big data" to "big experimentation".
Sharing your scoops to your social media accounts is a must to distribute your curated content. Not only will it drive traffic and leads through your content, but it will help show your expertise with your followers.
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Integrating your curated content to your website or blog will allow you to increase your website visitors’ engagement, boost SEO and acquire new visitors. By redirecting your social media traffic to your website, Scoop.it will also help you generate more qualified traffic and leads from your curation work.
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Creating engaging newsletters with your curated content is really easy.