The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them. 
Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network. 
The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other. 
The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.

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Statistical mechanics of complex networks

Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

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The Structure and Function of Complex Networks

A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known—a collaboration network and a food web—and find that it detects significant and informative community divisions in both cases.

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Community structure in social and biological networks

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.

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Finding and evaluating community structure in networks.

The Worst-Case Running Time of the Random Simplex Algorithm is Exponential in the Height

The present invention is directed towards methods and computer readable media for annotating and ranking user reviews on social review systems with inferred analytics. A reference framework is provided by creating context according to previous activity, bias, or background information of a given reviewer. The method of the present invention comprises receiving a first query identifying a given content item, generating a collection of content items based on one or more identical objective attributes associated with the given content item, identifying one or more subjective attributes associated with a given item in the collection of items, and providing a reference framework to interpret the subjective attributes associated with each item in the collection.

System and method for annotating and ranking reviews with inferred analytics

It is my great pleasure to welcome you to the 23rd International World Wide Web Conference, WWW 2014.

Since its inception in 1994, the International World Wide Web Conference (WWW) has become the premier forum in advancing Web technologies, introducing these technologies to the industry and to users, and promoting the development of Web standards. WWW is organized by the International World Wide Web Conference Steering Committee (IW3C2) in collaboration with local conference organizers of the host country.

This year we have the privilege of hosting WWW in Seoul, Korea for the very first time. International delegates can experience the advanced information technologies of Korea as well as both the contemporary culture and historical traditions of Seoul, which has been the capital city of Korea for the past six hundred years. I hope you are pleased, as I am, with the quality of this years program. There are 3 keynote speeches by world-class experts. The Research track presents 84 high quality papers; 107 posters of the Poster track report concise summaries of research; the Demo track shows 28 interesting demos; the Ph.D. Symposium track reports 11 papers of doctoral students; the Developers track presents 5 papers and 7 speeches focused on development experiences; the Industry track consists of 12 fascinating speeches from prominent industry leaders; the Web Science track presents 20 papers and 15 posters on novel interdisciplinary research; the W3C track is composed of sessions on the latest Web standards and emerging technologies; the Panel program consists of a plenary panel on the future of the Web and 3 panels on current issues. In addition to the tracks and special programs, workshops and tutorials are organized to report on-going works and to provide in-depth knowledge on important subjects, respectively; 19 workshops are organized to present 128 papers and provide 25 invited speeches; 13 tutorials are organized as lectures by experts. The content of the program is contained in the proceedings and the companion proceedings for your future reference.

Proceedings of the 23rd international conference on World wide web

A request is received, over a network from a requesting user to sort a plurality of comments relating to online content by a social status of a respective one of a plurality of commenters associated with each of the plurality of comments. The social status of the each of the plurality of commenters is determined using a computing device. The plurality of comments are sorted, using the computing device, by the social status of the respective one of the plurality of commenters associated with each of the plurality of comments. A representation of the sorted comments is transmitted, over the network, to the requesting user. The social status of commenters can reflect, in various embodiments, the status of commenters as favorite commenters of the requesting user, as members of the requesting user's social network, or as a top rated commenters in an online community.

System and method for social filtering of comments

Strings, such as Web pages or other documents, are fingerprinted in order to detect substantially similar strings, so as to avoid processing duplicate strings. At the same time determine a computerized method estimates the probability that a collision among fingerprints of dissimilar strings. As fingerprints are generated for strings presented for processing, when the fingerprint of a string is determined not to be identical to any fingerprint in a set of stored fingerprints, the new fingerprint is masked and the unmasked portion of the fingerprint is compared with a corresponding portion of the fingerprints in the stored set. Information is recorded regarding the number of matching masked fingerprints.

Andrei Z. Broder

Papers

The Worst-Case Running Time of the Random Simplex Algorithm is Exponential in the Height

System and method for annotating and ranking reviews with inferred analytics

Proceedings of the 23rd international conference on World wide web

System and method for social filtering of comments

Method for estimating the probability of collisions of fingerprints