Graph structure in the web Andrei Broder1, Ravi Kumar2, Farzin Maghoul1, Prabhakar Raghavan2, Sridhar Rajagopalan2, Raymie Stata3, Andrew Tomkins2, Janet Wiener3 1: AltaVista Company, San Mateo, CA. 2: IBM Almaden Research Center, San Jose, CA. 3: Compaq Systems Research Center, Palo Alto, CA. Abstract The study of the web as a graph is not only fascinating in its own right, but also yields valuable insight into web algorithms for crawling, searching and community discovery, and the sociological phenomena which characterize its evolution. We report on experiments on local and global properties of the web graph using two Altavista crawls each with over 200 million pages and 1.5 billion links. Our study indicates that the macroscopic structure of the web is considerably more intricate than suggested by earlier experiments on a smaller scale. Keywords: graph structure, diameter, web measurement 1. Designing crawl strategies on the web [Cho and Garcia-Molina 2000]. 1.1 Our main results 1.2 Related prior work
Six Degrees of Wikipedia Six Degrees of Wikipedia is a Harvard CS205 class project aimed at analyzing and understanding the structure of the English Wikipedia . The project was inspired by similar projects such as the Oracle of Bacon (which modeled the popular trivia game Six Degrees of Kevin Bacon ), Microsoft Academic Map (which visualized the concept of Erdos numbers ) and a 2008 version of Six Degrees of Wikipedia . Similar to these projects, our analysis goes beyond exploring the Six Degrees of Separation concept and attempts to provide insights into the overall graph structure of Wikipedia. The following 2-minute video presents the major components of the project: Is Wikipedia really a graph? The English Wikipedia is composed of almost 6 million different pages. Learning more about the project Visit the Graph Extraction Section to learn how the project extracted the Wikipedia graph by processing a 50GB dump of the English Wikipedia database obtained from that Freebase Wikipedia Extraction (WEX) project.
Aurelius | Applying Graph Theory and Network Science Interactome Part of the DISC1 interactome with genes represented by text in boxes and interactions noted by lines between the genes. From Hennah and Porteous, 2009. The word "interactome" was originally coined in 1999 by a group of French scientists headed by Bernard Jacq. Though interactomes may be described as biological networks, they should not be confused with other networks such as neural networks or food webs. Molecular interaction networks Molecular interactions can occur between molecules belonging to different biochemical families (proteins, nucleic acids, lipids, carbohydrates, etc.) and also within a given family. In fact, all interactome types are interconnected. Size of interactomes Estimates of the yeast protein interactome. Yeast. Genetic interaction networks Genes interact in the sense that they affect each other's function. Interactomics Methods of mapping the interactome The study of interactomes is called interactomics. Studied interactomes
Protein–Protein Interactions Essentials: Key Concepts to Building and Analyzing Interactome Networks Exploring complex networks : Article : Nature Steven H. Strogatz Networks are on our minds nowadays. Sometimes we fear their power — and with good reason. On 10 August 1996, a fault in two power lines in Oregon led, through a cascading series of failures, to blackouts in 11 US states and two Canadian provinces, leaving about 7 million customers without power for up to 16 hours1. In our lighter moments we play parlour games about connectivity. Meanwhile scientists have been thinking about networks too. Figure 1: Wiring diagrams for complex networks. a, Food web of Little Rock Lake, Wisconsin, currently the largest food web in the primary literature5. High resolution image and legend (73K) Why is network anatomy so important to characterize? From this perspective, the current interest in networks is part of a broader movement towards research on complex systems. But networks are inherently difficult to understand, as the following list of possible complications illustrates. Laser arrays provide a concrete example19, 20, 21, 22, 23, 24.
5 Graph Databases to Consider Of the major categories of NoSQL databases - document-oriented databases, key-value stores and graph databases - we've given the least attention to graph databases on this blog. That's a shame, because as many have pointed out it may become the most significant category. Graph databases apply graph theory to the storage of information about the relationships between entries. The relationships between people in social networks is the most obvious example. Google has its own graph computing system called Pregel (you can find the paper on the subject here), but there are several commercial and open source graph databases available. Neo4j This is one of the most popular databases in the category, and one of the only open source options. Neo Technologies cites several customers, though none of them are household names. Here's a fun illustration of how relationship data in graph databases works, from an InfoQ article by Neo Technologies COO Peter Neubauer: FlockDB AllegroGraph GraphDB InfiniteGraph
Overview of Bulbs, a Python Framework for Graph Databases like Neo4j | Bulbflow A Python framework for graph databases. Bulbs is an open-source Python persistence framework for graph databases and the first piece of a larger Web-development toolkit that will be released in the upcoming weeks. It’s like an ORM for graphs, but instead of SQL, you use the graph-traveral language Gremlin to query the database. Bulbs supports pluggable backends, and you can use it to connect to either Neo4j Server or Rexster. Neo4j Server is Neo4j‘s official server. This means your code is portable because you can to plug into different graph database backends without worrying about vendor lock in. Bulbs was developed in the process of building Whybase, a startup that will open for preview later this year. You can use Bulbs from within any Python Web-development framework, including Flask, Pyramid, and Django. Code Example Here’s how you model domain objects: And here’s how you use the models to create and connect domain objects: Why Graphs? Graph are an elegant way of storing relational data.
Force-based algorithms (graph drawing) Visualization of links between pages on a wiki using a force-directed layout. While graph drawing can be a difficult problem, force-directed algorithms, being physical simulations, usually require no special knowledge about graph theory such as planarity. Forces An alternative model considers a spring-like force for every pair of nodes where the ideal length of each spring is proportional to the graph-theoretic distance between nodes i and j, without using a separate repulsive force. Methods Once the forces on the nodes and edges of a graph have been defined, the behavior of the entire graph under these sources may then be simulated as if it were a physical system. It is also possible to employ mechanisms that search more directly for energy minima, either instead of or in conjunction with physical simulation. Advantages The following are among the most important advantages of force-directed algorithms: Good-quality results Flexibility Intuitive Simplicity Interactivity