There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Imagined communities: Reflections on the origin and spread of nationalism

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

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Community detection in graphs

The WWW has seen a massive growth in variety and usage of OSNs. The rising population of users on Twitter and its open nature has made it an ideal platform for various kinds of opportunistic pursuits, such as news and emergency communication, business promotion, political campaigning, spamming and spreading malicious content. Most of these opportunistic pursuits are exploited through automated programs, known as bots. In this study we propose a framework (Stweeler) to study bot impact and influence on Twitter from systems and social media perspectives.

Stweeler: A Framework for Twitter Bot Analysis

The literature of urban sociology and that of psychology have separately established two relationships: the first has linked characteristics of a community to its residents’ well-being, the second has linked well-being of individuals to their use of words. No one has hitherto explored the potential transitive relationship - that between characteristics of a community and its residents' use of words. We test this relationship by performing three steps. We consider Twitter users in a variety of London census communities; extract the subject matter of their tweets using "topic models"; and study the relationship between topics and community socio-economic well-being. We find that certain topics are correlated (positively and negatively) with community deprivation. Users in more deprived community tweet about wedding parties, matters expressed in Spanish/Portuguese, and celebrity gossips. By contrast, those in less deprived communities tweet about vacations, professional use of social media, environmental issues, sports, and health issues. We finally show that monitoring the subject matter of tweets not only offers insights into community well-being, but it is also a reasonable way of predicting community deprivation scores.

/pdf/talk-of-the-city-our-tweets-our-community-happiness-2y4gfkmsgv.pdf

Talk of the City: Our Tweets, Our Community Happiness

Evaluating DVE topology management and message propagation schemes requires avatar movement models. Most models are based on reasoned assumptions rather than measured data, potentially biasing evaluation. We measured player movement in World of Warcraft battlegrounds, and compared our observations against common assumptions about player avatar movement and navigation. We found that when modeling a highly interactive DVE such as a battleground, a waypoint model is not sufficient to describe most avatar movement. We were surprised to find that despite game incentives for grouping, the majority of avatar movement between objectives is individual, not grouped. Finally, we found that a hotspot-based model for avatar movement is consistent with our traces.

/pdf/avatar-movement-in-world-of-warcraft-battlegrounds-3dsazc0edc.pdf

Avatar movement in World of Warcraft battlegrounds

Online social networks such as Facebook and Twitter have started allowing users to tag their posts with geographical coordinates collected through the GPS interface of users smartphones. While this information is quite useful and already indicative of user behavior, it also lacks some semantics about the type of place the user is (e.g., restaurant, museum, school) which would allow a better understanding of users' patterns. While some location based online social network services (e.g., Foursquare) allow users to tag the places they visit, this is not an automated process but one which requires the user help. In this paper we exploit the dynamics of human activity to associate categories to GPS coordinates of social network posts. We have collected geo-tagged tweets of a large city through Twitter. A supervised learning framework takes the tweets spatial-temporal features and determines human dynamics which we use to infer the place category. Our results over the data show that the prediction framework is able to accurately identify if a place is of a certain category given its user activity patterns. The average accuracy is about 70%, reaching the highest accuracy for work (90%) and educational places (80%). Moreover the framework identifies the category of a place, with an accuracy up to 66%, finding out where people eat and drink, go for entertainment, or work/study.

/pdf/what-is-this-place-inferring-place-categories-through-user-2xbx42ng5n.pdf

What is this place? Inferring place categories through user patterns identification in geo-tagged tweets

This paper is about the evolution of the Internet from a simple data network into a true multiservice network that can support the emerging multimedia applications and their protocols with appropriate performance and costs. The real-time delivery and specific bandwidth requirements of these multimedia applications have created a need for an integrated services Internet in which traditional best effort datagram delivery can coexist with additional enhanced quality of service delivery classes. The integrated services Internet will be able to commit to meet bandwidth, packet loss, and delay specifications for individual data flows by using the resource reservation protocol together with appropriate packet forward scheduling policies.

Jon Crowcroft

Papers

Stweeler: A Framework for Twitter Bot Analysis

Talk of the City: Our Tweets, Our Community Happiness

Avatar movement in World of Warcraft battlegrounds

What is this place? Inferring place categories through user patterns identification in geo-tagged tweets

The integrated services in the Internet: state of the art