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.

A system is provided for assessing an effectiveness of online advertising by recording the context in which each advertisement is provided and tracking whether each advertisement resulted in a consumer response. When a request for an online advertisement is received, a unique code, which can be utilized to redeem a coupon, is generated and provided with an advertisement. Contextual information associated with providing the online advertisement is recorded for the unique code. Contextual information can include, for example, information about the provided advertisement, information about how the advertisement will be presented, information about the potential viewer for the advertisement, and the like. If the unique code is later utilized to redeem the coupon, the redemption is recorded for the unique code so that an online advertiser can assess the effectiveness of their online advertisements in relation to various contexts in which their advertisements are provided.

Assessing ad value

We study the performance of packet routing on arrays (or meshes) with bounded buffers in the routing switches, assuming that new packets are continuously inserted at all the nodes. We give the first routing algorithm on this topology that is stable under an injection rate within a constant factor of the hardware bandwidth. Unlike previous results, our algorithm does not require the global synchronization of the insertion times or the retraction and reinsertion of excessively delayed messages and our analysis holds for a broad range of packet generation stochastic distributions. This result represents a new application of a general technique for the design and analysis of dynamic algorithms that we first presented in [Broder et al., FOCS 96, pp. 390–399.].

Dynamic Packet Routing on Arrays with Bounded Buffers

Display advertising is one of the two major advertising channels on the web (in addition to search advertising). Display advertising on the Web is usually done by graphical ads placed on the publishers' Web pages. There is no explicit user query, and the ad selection is performed based on the page where the ad is placed (contextual targeting) or user's past activities (behavioral targeting). In both cases, sophisticated text analysis and learning algorithms are needed to provide relevant ads to the user. In this tutorial we will overview the display advertising marketplace, and technologies that power the display advertising platforms.

Introduction to display advertising: a half-day tutorial

The twin disciplines of Pessimal Algorithm Design and Simplexity Analysis are introduced and illustrated by means of representative problems

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Pessimal algorithms and simplexity analysis

The Web graph, meaning the graph induced by Web pages as nodes and their hyperlinks as directed edges, has become a fascinating object of study for many people: physicists, sociologists, mathematicians, computer scientists, and information retrieval specialists.Recent results range from theoretical (e.g.: models for the graph, semi-external algorithms), to experimental (e.g.: new insights regarding the rate of change of pages, new data on the distribution of degrees), to practical (e.g.: improvements in crawling technology).Recent results range from theoretical (e.g.: models for the graph, semi-external algorithms), to experimental (e.g.: new insights regarding the rate of change of pages, new data on the distribution of degrees), to practical (e.g.: improvements in crawling technology).The goal of this talk is to convey an introduction to the state of the art in this area and to sketch the current issues in collecting, representing, analyzing, and modeling this graph. Although graph analytic methods are essential tools in the Web IR arsenal, they are well known to the SIGIR community and will not be discussed here in any detail; instead, we will explore some challenges and opportunities for using IR methods and techniques in the exploration of the Web graph, in particular in dealing with legitimate and "spam" perturbations of the "natural" process of birth and death of nodes and links, and conversely, the challenges and opportunities of using graph methods in support of IR on the Web and in the enterprise.

Andrei Z. Broder

Papers

Assessing ad value

Dynamic Packet Routing on Arrays with Bounded Buffers

Introduction to display advertising: a half-day tutorial

Pessimal algorithms and simplexity analysis

Keynote Address - exploring, modeling, and using the web graph