Machine Learning/Kaggle Social Network Contest/Network Description - Revision history

SpammerHellDontDelete at 02:19, 9 December 2010

2010-12-09T02:19:38Z

← Older revision		Revision as of 19:19, 8 December 2010
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	*** sum([http://cneurocvs.rmki.kfki.hu/igraph/doc/R/neighborhood.html neighborhood.size](dg, 1, nodes=myGuys, mode="out"))		*** sum([http://cneurocvs.rmki.kfki.hu/igraph/doc/R/neighborhood.html neighborhood.size](dg, 1, nodes=myGuys, mode="out"))
	*** mode = "in", "out" or "all"		*** mode = "in", "out" or "all"

			==How the test set was created ==
			The test set seems to have been generated by randomly picking out nodes from the the outnode column and in nodes from the in node column. ie Not just randomly creating edges between any nodes.
			* All test out nodes are in the training out node column
			* All but 13 test in nodes are in the training in node column

Jjhale: /* Conectivity */

2010-11-24T08:07:05Z

Conectivity

← Older revision		Revision as of 01:07, 24 November 2010
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	Is it more likely that clusters were created by removing nodes or that they merged due to randomly adding nodes?		Is it more likely that clusters were created by removing nodes or that they merged due to randomly adding nodes?
	* TODO figure out probs of adding and removing nodes under different sampling hypotheses.		* TODO: figure out probs of adding and removing nodes under different sampling hypotheses.
			* TODO: identify the edges which are merging the clusters
	* I'm guessing that the chances of a randomly generated edge joins the small clusters is very low.		* I'm guessing that the chances of a randomly generated edge joins the small clusters is very low.

Jjhale: /* Conectivity */ effect of adding test data on clusters

2010-11-24T07:53:54Z

Conectivity: effect of adding test data on clusters

@@ Line 9: / Line 9: @@
 "A digraph is strongly connected if every vertex is reachable from every other following the directions of the arcs. On the contrary, a digraph is weakly connected if its underlying undirected graph is connected. A weakly connected graph can be thought of as a digraph in which every vertex is "reachable" from every other but not necessarily following the directions of the arcs. A strong orientation is an orientation that produces a strongly connected digraph." [http://en.wikipedia.org/wiki/Glossary_of_graph_theory wikipedia]
-* The Graph is '''not''' weakly connected
+* The Training Graph is '''not''' weakly connected
 * It contains 27 subgraphs This means that it can be broken down into at least two discrete subgraphs.
 ** c.f. [http://cneurocvs.rmki.kfki.hu/igraph/doc/R/clusters.html igraph clustering]
@@ Line 38: / Line 38: @@
 |    1
 |}
 * Diameter of the directed graph is 14
 ** This is the longest of the shortest directed paths between two nodes

SpammerHellDontDelete: /* Conectivity */

2010-11-23T20:19:31Z

Conectivity

← Older revision		Revision as of 13:19, 23 November 2010
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	* It contains 27 subgraphs This means that it can be broken down into at least two discrete subgraphs.		* It contains 27 subgraphs This means that it can be broken down into at least two discrete subgraphs.
	** c.f. [http://cneurocvs.rmki.kfki.hu/igraph/doc/R/clusters.html igraph clustering]		** c.f. [http://cneurocvs.rmki.kfki.hu/igraph/doc/R/clusters.html igraph clustering]
	** There is one very large cluster containing all but 154 verticies, then 4 with size 10 - 37, 8 sized 3 - 7, 13 size 2 ~~and one lonely dude.~~		** There is one very large cluster containing all but 154 verticies, then 4 with size 10 - 37, 8 sized 3 - 7 and 13 size 2
			** note that igraph seems to create a vertex labelled 0 but the labels in the traindata file range from 1 to 1133547

	* I also grabbed the number of strongly connected subgraphs		* I also grabbed the number of strongly connected subgraphs

SpammerHellDontDelete: /* Conectivity */

2010-11-23T17:51:21Z

Conectivity

← Older revision		Revision as of 10:51, 23 November 2010
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	\| 1		\| 1
	\|}		\|}
	* Diameter of the directed graph		* Diameter of the directed graph is 14
	** This is the longest of the shortest directed paths between two nodes		** This is the longest of the shortest directed paths between two nodes
	** R igraph		** R igraph

Jjhale at 03:59, 23 November 2010

2010-11-23T03:59:59Z

@@ Line 6: / Line 6: @@
 * number of edges: 7,237,983
-* The Graph is '''not''' weakly connected! This means that it can be broken down into at least two discrete subgraphs.
+== Conectivity ==
 * Diameter of the directed graph
 ** This is the longest of the shortest directed paths between two nodes

Jjhale at 03:29, 23 November 2010

2010-11-23T03:29:11Z

← Older revision		Revision as of 20:29, 22 November 2010
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	* Total number of nodes: 1,133,547		* Total number of nodes: 1,133,547
	* number of edges: 7,237,983		* number of edges: 7,237,983

			* The Graph is '''not''' weakly connected! This means that it can be broken down into at least two discrete subgraphs.

	* Diameter of the directed graph		* Diameter of the directed graph

Jjhale: Created page with 'Here we can put the descriptive statistics of the network: * Number of fully sampled nodes: 37,689 ** ie the unique "outnodes" in the edge list * Total number of nodes: 1,133,5…'

2010-11-23T03:26:56Z

Created page with 'Here we can put the descriptive statistics of the network: * Number of fully sampled nodes: 37,689 ** ie the unique "outnodes" in the edge list * Total number of nodes: 1,133,5…'

New page

Here we can put the descriptive statistics of the network:

* Number of fully sampled nodes: 37,689
** ie the unique "outnodes" in the edge list
* Total number of nodes: 1,133,547
* number of edges: 7,237,983

* Diameter of the directed graph
** This is the longest of the shortest directed paths between two nodes
** R igraph
*** diameter (dg, directed = TRUE, unconnected = TRUE)
*** Was taking forever so I aborted (after 34 minutes...)
* Total number of direct neighbours out: 7 275 672, in: 508 688, all: 7 473 273
** For each of our 38k I calculated the number of outbound neighbours and summed it
** R igraph:
*** sum([http://cneurocvs.rmki.kfki.hu/igraph/doc/R/neighborhood.html neighborhood.size](dg, 1, nodes=myGuys, mode="out"))
*** mode = "in", "out" or "all"