Neo4j is a powerful, award-wining graph database written in Java. It can store billions of nodes and relationships and allows very fast query/traversal. We release today a new version of the Neo4j Plugin supporting the latest 1.5 version of Neo4j. In Gephi, go to Tools > Plugins to install the plug-in.
The plugin let you visualize a graph stored in a Neo4j database and play with it. Features include full import, traversal, filter, export and lazy loading.
The new version of the build-in layout ForceAtlas is now released. It is scaled for small to medium-size graphs, and is adapted to qualitative interpretation of graphs. The equations are the same as ForceAtlas 1, but there are more options and innovative optimizations that make it a very fast layout algorithm.
It is good enough to deal with very small graphs (10 nodes) and fast enough to spatialize 10,000 nodes graphs in few minutes, with the same quality. If you have time, it can deal with even bigger graphs.
Update Gephi (Help > Check for Updates) to get this new layout.
Force Atlas 2:
Is a continuous algorithm, that allows you to manipulate the graph while it is rendering (a classic force-vector, like Fruchterman Rheingold, and unlike OpenOrd)
Has a linear-linear model (attraction and repulsion proportional to distance between nodes). The shape of the graph is between Früchterman & Rheingold’s layout and Noack’s LinLog.
Features a unique adaptive convergence speed that allows most graphs to converge more efficiently
Proposes summarized settings, focused on what impact the shape of the graph (scaling, gravity…). Default speed should be the good one.
Now features a Barnes Hut optimization (performance drops less with big graphs)
Force Atlas 2 features these settings:
Scaling: How much repulsion you want. More makes a more sparse graph.
Gravity: Attracts nodes to the center. Prevents islands from drifting away.
Dissuade Hubs: Distributes attraction along outbound edges. Hubs attract less and thus are pushed to the borders.
LinLog mode: Switch ForceAtlas’ model from lin-lin to lin-log (tribute to Andreas Noack). Makes clusters more tight.
Prevent Overlap: Use only when spatialized. Should not be used with “Approximate Repulsion”
Tolerance (speed): How much swinging you allow. Above 1 discouraged. Lower gives less speed and more precision.
Approximate Repulsion: Barnes Hut optimization: n² complexity to n.ln(n) ; allows larger graphs.
Approximation: Theta of the Barnes Hut optimization.
Edge Weight Influence: How much influence you give to the edges weight. 0 is “no influence” and 1 is “normal”.
Force Atlas 2 was created by Mathieu Jacomy at the Sciences Po Médialab (Paris), founding member of the Gephi Consortium.
Cezary Bartosiak and Rafał Kasprzyk just released the Complex Generators plugin, introducing many awaited scientific generators. These generators are extremely useful for scientists, as they help to simulate various real networks. They can test their models and algorithms on well-studied graph examples. For instance, the Watts-Strogatz generator creates networks as described by Duncan Watts in his Six Degrees book.
The plugin contains the following generators:
Balanced Tree
Barabasi Albert
Barabasi Albert Generalized
Barabasi Albert Simplified A
Barabasi Albert Simplified B
Erdos Renyi Gnm
Erdos Renyi Gnp
Kleinberg
Watts Strogatz Alpha
Watts Strogatz Beta
The plug-in can be installed directly from Gephi 0.8, from the Plugins menu.
The HTTP Graph plugin provides real-time collection and visualization of HTTP traffic. Using the embeddable Membrane Router, details are extracted from the transaction headers and fed to Gephi for graphing and further analysis. This approach makes the relationships between clients, servers, and resources easily visible.
There are 4 types of nodes: client, uri, host, domain.
Client: By default, the largest sized nodes with the source IP addresses of clients for labels. If you are the only one pointing to the plugin’s proxy, there will probably be only one of these nodes that says 127.0.0.1. Clients are linked to a domain node of ‘local’ to keep them together on the graph. Another function of the client node is to keep the graph anchored. You may find it interesting to use a filter in Gephi to hide the client type nodes to see a more “free-form” graph of the internet. If you do this, you may see large pieces float away because they didn’t link to the rest of the graph anymore!
URI: By default, the smallest sized nodes with no visible labels. These represent resources like HTML pages, images, javascript, or whatever other resources might be requested through the proxy. The size in bytes and the MIME (Content-Type) reported by the host when returning the resource is available so you can see what it is.
Host: For a given domain (.gephi.org, .google.com, etc.) there can be multiple hosts which serve the different resources. In some cases, you may see the same resource being served from multiple servers in a DNS-based load balancing system. Other interesting details about the underlying architecture of the sites you are viewing can be seen.
Domain: These nodes exist primarily to keep the related hosts close together on the graph. You may want to use a filter in Gephi for this type of node and hide them to see a different perspective.
Edges
HTTP and the web are defined by links, which are essentially directed graph edges, and these occur at the resource level. An HTML page resource will link to CSS, image, and other file resources, both on the same domain, and on remote domains. These inter-domain links are the glue that forms the structure of the world wide web.
Have fun!
~by phreakocious
Get the HTTP Graph plugin on the Plugins Center, or in Gephi go to Tools > Plugins > Available plugins.
This is a preliminary result of the network of retweets with the hashtag #jan25 at February 11 2011, at the time of the announcement of Mubarak’s resignation. If you retweeted someone, or has been retweeted, it is possible that your username is one of these tinny points (or maybe a bigger one?).
To collect the network data, I used the Gephi Graph Streaming plugin, connected it to a Python web server I made myself. This web server works like a bridge, it connects to the Twitter Streaming API using the statuses/filter service and converts the users and retweets to nodes and edges in a network format that can be read by the Gephi Graph Streaming plugin. Nodes are twitter users, and links appear between the nodes A and B when B retweeted a message of A containing the hashtag #jan25.
The static network visualization is just the final result of about one hour of data collection. It is a dynamic network, and it’s possible to get much more information from the collected data. For example, before the announcement, there were few nodes and edges, sparse in time. But when the announcement arrives, a boom of retweets appears on the network. A video with the flow of retweets is available on YouTube. It shows the dynamic network construction during the hour of data collection, compacted in less than four minutes. During the collection, I run Gephi with the Force Atlas layout just adjusting some parameters from default: repulsion strength to 2000, attraction strength to 0.3 and speed to 10.
I was very lucky to get this data. On February 11 afternoon I was testing the Python server that works as bridge and connected to Twitter. I tried some interesting hashtags to see it working, and at the moment #jan25 seemed to be an active hashtag. I let the application run for some time, adjusted some parameters for visualization, and at some point there was a burst in the activity. I didn’t understood what was happening until I checked again my Twitter account and realized that the Egypt’s vice-president had just made the resignation announcement. After it, I proceeded collecting data, and the final result was this network. It was very interesting to see, in real time, the exact moment when Tahrir Square, from a mass protest demonstration, has been transformed in a giant party, and the burst in the Twitter’s activity. It was like covering in real time a virtual event, a big event that was happening in the Twitter virtual world.
After playing with the data, I found that the data I got through the Twitter Streaming API is only approximately 10% of the total. I’m now working to recover all data and hopeful soon I can make available the full graph of retweets.
Dataset available in a GEXF file here. Download it and play with it with Gephi!
—– This work is part of a research project involving the Computer Science Department of the University of Turin (www.di.unito.it), the Complex Networks and Systems Group of the ISI Foundation (www.isi.it), and the Informatics department of Indiana University (http://cnets.indiana.edu/).
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Update Gephi (Help > Check for Updates) to get this new layout.
Get the HTTP Graph plugin on the 
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