Discovering Social Events through Online Attention

Twitter is a major social media platform in which users send and read messages (“tweets”) of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy, with hundreds of millions of tweets sent every day, it is difficult to differentiate between times of turmoil and times of typical discussion. In this work we present a new approach to addressing this problem. We first assess several possible “thermostats” of activity on social media for their effectiveness in finding important time periods. We compare methods commonly found in the literature with a method from economics. By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter. We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011.     

Discovering tumor suppressor genes through genome-wide copy number analysis

Classical tumor suppressor gene discovery has largely involved linkage analysis and loss-of-heterozygosity (LOH) screens, followed by detailed mapping of relatively large chromosomal regions. Subsequent efforts made use of genome-wide PCR-based methods to detect rare homozygous deletions. More recently, high-resolution genomic arrays have been applied to cancer gene discovery. However, accurate characterization of regions of genomic loss is particularly challenging due to sample heterogeneity, the small size of deleted regions and the high frequency of germline copy number polymorphisms. Here, we review the application of genome-wide copy number analysis to the specific problem of identifying tumor suppressor genes.     

Discovering Link Communities in Complex Networks by an Integer Programming Model and a Genetic Algorithm

Identification of communities in complex networks is an important topic and issue in many fields such as sociology, biology, and computer science. Communities are often defined as groups of related nodes or links that correspond to functional subunits in the corresponding complex systems. While most conventional approaches have focused on discovering communities of nodes, some recent studies start partitioning links to find overlapping communities straightforwardly. In this paper, we propose a new quantity function for link community identification in complex networks. Based on this quantity function we formulate the link community partition problem into an integer programming model which allows us to partition a complex network into overlapping communities. We further propose a genetic algorithm for link community detection which can partition a network into overlapping communities without knowing the number of communities. We test our model and algorithm on both artificial networks and real-world networks. The results demonstrate that the model and algorithm are efficient in detecting overlapping community structure in complex networks.     

Discovering gorilla

On July 16, 1847, a missionary newly arrived in New York City from West Africa packed a collection of bones in a box and shipped them off to a colleague in Massachusetts. In a letter, Thomas S. Savage (Fig. 1), a graduate of Yale College and Yale Medical School, admitted to being “quite unwell,” probably meaning “utterly wretched.” He had already endured tropical diseases in Liberia off and on for more than a decade, and he'd seen his first two wives languish and die there, probably of malaria. He wasn't the sort to complain lightly.     

Discovering Precise Temporal Patterns in Large-Scale Neural Recordings through Robust and Interpretable Time Warping

1. Download : Download high-res image (224KB)
2. Download : Download full-size image

     

Qualities and Inequalities in Online Social Networks through the Lens of the Generalized Friendship Paradox

The friendship paradox is the phenomenon that in social networks, people on average have fewer friends than their friends do. The generalized friendship paradox is an extension to attributes other than the number of friends. The friendship paradox and its generalized version have gathered recent attention due to the information they provide about network structure and local inequalities. In this paper, we propose several measures of nodal qualities which capture different aspects of their activities and influence in online social networks. Using these measures we analyse the prevalence of the generalized friendship paradox over Twitter and we report high levels of prevalence (up to over 90% of nodes). We contend that this prevalence of the friendship paradox and its generalized version arise because of the hierarchical nature of the connections in the network. This hierarchy is nested as opposed to being star-like. We conclude that these paradoxes are collective phenomena not created merely by a minority of well-connected or high-attribute nodes. Moreover, our results show that a large fraction of individuals can experience the generalized friendship paradox even in the absence of a significant correlation between degrees and attributes.