pagerank

hadoop pagerank error when running

由 自古美人都是妖i 提交于 2019-12-06 04:36:14
问题 I have installed hadoop on my vmware and designed my jar file pagerank. Running the following command: hadoop jar PageRank-1.0.0.jar PageRankDriver init input output 2, I get the following error; Failing this attempt.Diagnostics: [2017-12-01 12:55:58.278]Exception from container-launch. Container id: container_1512069161738_0011_02_000001 Exit code: 1 Stack trace: ExitCodeException exitCode=1: at org.apache.hadoop.util.Shell.runCommand(Shell.java:994) at org.apache.hadoop.util.Shell.run(Shell

How to handle huge sparse matrices construction using Scipy?

由 老子叫甜甜 提交于 2019-12-05 18:31:29
So, I am working on a Wikipedia dump to compute the pageranks of around 5,700,000 pages give or take. The files are preprocessed and hence are not in XML. They are taken from http://haselgrove.id.au/wikipedia.htm and the format is: from_page(1): to(12) to(13) to(14).. from_page(2): to(21) to(22).. . . . from_page(5,700,000): to(xy) to(xz) so on. So. basically it's a construction of a [5,700,000*5,700,000] matrix, which would just break my 4 gigs of RAM. Since, it is very-very Sparse, that makes it easier to store using scipy.lil.sparse or scipy.dok.sparse , now my issue is: How on earth do I

How does pageranking algorithm deal with webpage without outbound links?

由 [亡魂溺海] 提交于 2019-12-05 08:27:45
I am learning about the PageRanking algorithm so sorry for some newbie questions. I understand that the PR value is calculated for each page by the summation of incoming links to itself. Now I am bothered by a statement which stated that "the PageRank values sum to one " at wikipedia . As the example shown at wikipedia, if every page has a outbound link, then the summation of whole probabilities from each page should be one. However, if a page does not have any outbound link such as page A at the example, then the summation should not be value 1 right ? Thus, does Pagerank algorithm have to

Possible to get alexa information or google page rankings over time?

由 两盒软妹~` 提交于 2019-12-05 07:21:57
问题 I am trying to access historical google page rankings or alexa rankings over time to add some weightings on a search engine I am making for fun. This would be a separate function that I would call in Python (ideally) and pass in the paramaters of the URL and how long I wanted to get the average over, measured in days and then I could just use that information to weight my results! I think it could be fun to work on, but I also feel that this may be easy to do with some trick of the APIs some

Networkx: Differences between pagerank, pagerank_numpy, and pagerank_scipy?

由 試著忘記壹切 提交于 2019-12-03 05:11:20
问题 Does anyone know about the differences in accuracy between the three different pagerank functions in Networkx? I have a graph of 1000 nodes and 139732 edges, and the "plain" pagerank function didn't seem to work at all -- all but two of the nodes had the same PG, so I'm assuming this function doesn't work quite as well for large graphs? pagerank_numpy 's values also seemed to be a little bit more spread out than pagerank_scipy 's values. The documentation for this function says that "This

Implementing PageRank using MapReduce

由 雨燕双飞 提交于 2019-12-03 04:40:58
问题 I'm trying to get my head around an issue with the theory of implementing the PageRank with MapReduce. I have the following simple scenario with three nodes: A B C. The adjacency matrix is here: A { B, C } B { A } The PageRank for B for example is equal to: (1-d)/N + d ( PR(A) / C(A) ) N = number of incoming links to B PR(A) = PageRank of incoming link A C(A) = number of outgoing links from page A I am fine with all the schematics and how the mapper and reducer would work but I cannot get my

Getting Good Google PageRank [closed]

由 旧巷老猫 提交于 2019-12-03 03:57:06
问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 8 years ago . In SEO people talk a lot about Google PageRank. It's kind of a catch 22 because until your site is actually big and you don't really need search engines as much, it's unlikely that big sites will link to you and increase your PageRank! I've been told that it's easiest to simply get a couple high quality links to

Keyword analysis in PHP

由 泪湿孤枕 提交于 2019-12-03 01:13:43
问题 For a web application I'm building I need to analyze a website, retrieve and rank it's most important keywords and display those. Getting all words, their density and displaying those is relatively simple, but this gives very skewed results (e.g. stopwords ranking very high). Basically, my question is: How can I create a keyword analysis tool in PHP which results in a list correctly ordered by word importance? 回答1: Recently, I've been working on this myself, and I'll try to explain what I did

Networkx: Differences between pagerank, pagerank_numpy, and pagerank_scipy?

由 左心房为你撑大大i 提交于 2019-12-02 18:25:51
Does anyone know about the differences in accuracy between the three different pagerank functions in Networkx? I have a graph of 1000 nodes and 139732 edges, and the "plain" pagerank function didn't seem to work at all -- all but two of the nodes had the same PG, so I'm assuming this function doesn't work quite as well for large graphs? pagerank_numpy 's values also seemed to be a little bit more spread out than pagerank_scipy 's values. The documentation for this function says that "This will be the fastest and most accurate for small graphs." What is meant by "small" graphs? Also, why doesn

Implementing PageRank using MapReduce

由 淺唱寂寞╮ 提交于 2019-12-02 17:46:07
I'm trying to get my head around an issue with the theory of implementing the PageRank with MapReduce. I have the following simple scenario with three nodes: A B C. The adjacency matrix is here: A { B, C } B { A } The PageRank for B for example is equal to: (1-d)/N + d ( PR(A) / C(A) ) N = number of incoming links to B PR(A) = PageRank of incoming link A C(A) = number of outgoing links from page A I am fine with all the schematics and how the mapper and reducer would work but I cannot get my head around how at the time of calculation by the reducer, C(A) would be known. How will the reducer,
订阅 pagerank