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Andrei Z. Broder

Other affiliations: AmeriCorps VISTA, IBM, Columbia University  ...read more
Bio: Andrei Z. Broder is an academic researcher from Google. The author has contributed to research in topics: Web search query & Web query classification. The author has an hindex of 67, co-authored 241 publications receiving 27310 citations. Previous affiliations of Andrei Z. Broder include AmeriCorps VISTA & IBM.


Papers
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Proceedings Article
01 Jan 2000

6 citations

Patent
27 Mar 2008
TL;DR: In this paper, a system and method are disclosed for rewriting queries into a bid phrase for identifying search results and/or advertisements, and a mapping between potential queries and bid phrases may be generated.
Abstract: A system and method are disclosed for rewriting queries. The queries may be rewritten into a bid phrase for identifying search results and/or advertisements. The bid phrase may be a keyword that is purchased for sponsored searching. A mapping between potential queries and bid phrases may be generated. The mapping may be referenced upon receiving a search query for identifying a query rewrite with a bid phrase for that search query. The mapping may be generated in preprocessing.

6 citations

Journal ArticleDOI
TL;DR: In this article, it was shown that any randomized sampling scheme for the relative intersection of sets based on testing equality of samples yields an equivalent min-wise independent family, and in a certain sense, minwise independent families are complete for this type of estimation.
Abstract: We provide several new results related to the concept of min-wise independence. Our main result is that any randomized sampling scheme for the relative intersection of sets based on testing equality of samples yields an equivalent min-wise independent family. Thus, in a certain sense, min-wise independent families are “complete” for this type of estimation.

6 citations

01 Jan 2008
TL;DR: This work proposes a method for automatically using this knowledge to augment traditional IR systems, using contextual advertising as an application domain, and can actually learn that a query “menu” is likely to have food connotations on FLICKR but user interface connotation on DEL.ICIO.US.
Abstract: Folksonomies allow users to collaboratively tag a variety of textual and multimedia objects with sets of labels. The largest folksonomy projects, such as FLICKR and DEL.ICIO.US, contain millions of multi-labeled objects, and embed significant amounts of human knowledge. We propose a method for automatically using this knowledge to augment traditional IR systems, using contextual advertising as an application domain. Given a query, we first identify a set of relevant tags, and then use tags that cooccur with them to augment the query. Importantly, our method performs domainspecific query disambiguation, and can actually learn that a query “menu” is likely to have food connotation on FLICKR but user interface connotation on DEL.ICIO.US.

6 citations

01 May 1985
TL;DR: A study of the general properties of permutation invariant mappings combined with the analysis of this particular distribution made possible the computation of the expected running time of this factorization method, settling an open conjecture of Pollard.
Abstract: : A random mapping is a random graph where ever vertex has outdegree one. Previous work was concerned mostly with a uniform probability distribution on these mappings. In contrast, this investigation assumed a non-uniform model, where differ mappings have different probabilities. An important application is the analysis of factorization heuristic due to Pollard and Brent. The model involved is a random mapping where every vertex has indegree either 0 or d. This distribution belongs to class called permutation invariant. A study of the general properties of permutation invariant mappings combined with the analysis of this particular distribution made possible the computation of the expected running time of this factorization method, settling an open conjecture of Pollard.

6 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
Abstract: 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.

18,415 citations

Journal ArticleDOI
TL;DR: Developments in this field are reviewed, 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.
Abstract: 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.

17,647 citations

Journal ArticleDOI
TL;DR: This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
Abstract: 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.

14,429 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: 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.).

13,246 citations

Journal ArticleDOI
TL;DR: It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.
Abstract: 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.

12,882 citations