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  1. 1. S.Geetha, K.Sathiya Kumari / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1748-1755 Survey on Link Prediction and Page Ranking In Blogs S.Geetha K.Sathiya Kumari M.Phil Scholar Assistant Professor Department Of Computer Science Department Of Computer Science PSGR Krishnammal College For Women PSGR Krishnammal College For Women Coimbatore CoimbatoreABSTRACT This paper presents a study of the and exploited, through web-based groupsvarious aspects of link prediction and page established for that purpose.ranking in blogs. Social networks have taken on anew eminence from the prospect of the analysis 1.1 Social Mediaof social networks, which is a recent area of Social media analysis is the practice ofresearch which grew out of the social sciences as gathering data from blogs and social mediawell as the exact sciences, especially with the websites, such as Twitter, Face book, Digg andcomputing capacity for mathematical Delicious, and analyzing that data to informcalculations and even modelling which was business decisions. The most common use of socialpreviously impossible. An essential element of media analytics is gauging customer opinion tosocial media, particularly blogs, is the hyperlink support marketing and customer service activities.graph that connects various pieces of content. There are number of types of tools foe variousLink prediction has many applications, including functions in the social media analytics process.recommending new items in online networks These tools include application to identify the best(e.g., products in eBay and Amazon, and friends social media sites to serve the purposes, applicationsin Face book), monitoring and preventing to harvest data, a storage product or service, andcriminal activities in a criminal network, data analytics software. However, text analysis andpredicting the next web page users will visit, and sentiment analysis technologies are the foundationalcomplementing missing links in automatic web components of social media analytics.data crawlers. Page Rank is the technique usedby Google to determine importance of page on 1.2 Classification of social mediathe web. It considers all incoming links to a page Social media technologies take on manyas votes for Page Rank. Our findings provide an different forms including magazines, Internetoverview of social relations and we address the forums, web logs, social blogs, microblogging,problem of page ranking and link prediction in wikis, social networks, pod casts, photographs ornetworked data, which appears in many pictures, video, rating and social book marking. Byapplications such as network analysis or applying set of theories in the field of mediarecommended systems. research and social processes Kaplan and Heanlein created a classification scheme for different socialKeywords- web log, social networks analysis, media types in their business Horizons articlereadership, link prediction, Page ranking. published in 2010. According to Andreas Kalpan and MichaelI. INTRODUCTION Heinlein there are six different types of social Data mining is the practice of automatically media: collaborative projects (e.g., Wikipedia),searching large stores of data to discover patterns blogs and micro blogs (e.g., Twitter), contentand trends that go beyond simple analysis. Data communities (e.g., You Tube), social networkingmining uses sophisticated mathematical algorithms sites (e.g., Face book), virtual game words (e.g.,to segment the data and evaluate the probability of World of Watercraft), and virtual social words (e.g.,future events. Data mining is also known as Second Life). Technologies include: blogs, pictureKnowledge Discovery in Data (KDD). Data mining sharing, blogs, wall-postings, email, instanttools predict future trends and behaviors, allowing messaging, music-sharing, crowd sourcing andbusinesses to make proactive, knowledge-driven voice over IP, to name a few. Many of these socialdecisions. Social networking is the practice of media services can be integrated via social networkexpanding the number of one‟s business and/or aggregation platforms. Social media networksocial contacts by making connections through websites include sites like Face book, Twitter, Beboindividuals. While social networking gone on almost and My space.as long as societies themselves have existed, theunparalleled potential of the Internet to promotesuch connections is only now being fully recognized 1748 | P a g e
  2. 2. S.Geetha, K.Sathiya Kumari / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1748-17551.3 Realities of social media mining networking refers to the process of capturing the The data mining of social media activity is social and societal characteristics of networkednow commonplace in business intelligence circles. structures or communities over the Web. SocialAnything and everything on the internet is fair game networking research involves in the combination ofextreme data mining practices. Once something is a variety of research paradigms, such as Webpushed out to the World Wide Web, it will forever mining, Web communities, social network analysisbe fodder for a business intelligence or data mining and behavioral and cognitive modeling and so on.application somewhere in the cyberspace universe.Content created by a social network‟s users to 1.5 Blogsoutside websites, advertisers, and affiliates for data (Thomas) Ablog-a shorthand term thatmining an important component of the social means “Web log”-is an online, chronologicalnetworking business model, as data mining collection of personal communitary and links. Easymethodologies progress far beyond traditional to create and use from anywhere with an internetdemographic profiling into interpolation and connection, blogs are a form of internet publishingstatistical modeling based on swarms and cluster that has become an established communicationsgroups. tool. Blogs represent an alternative to mainstream media publications. The personal perspectives So powerful and lucrative is social media presented on blogs often lead to discourse betweendata mining that governments around the globe have bloggers, and many blog circles generate a strongbegun to carefully scrutinize the need for regulation sense of community. Blogs are highly volatile.in this apace, especially with respect to protecting Bloggers can edit or delete posts, and this transientthe privacy of their citizen‟s that post data to these nature can make blogs difficult to archive or index.networks. While some regulation is probably Blogs are proliferating rate. Estimates suggest asneeded, what concerns user is when the legislators many as 50 million people are now blogging.of the free world start demanding social networks Because blogs are easy to create and modify, theyinvoluntarily hand over their user-generated content occupy a unique niche in cyberspace- that of highlyin order to better enable central governments to personalized discussion forums that fostercarry out their own “citizen intelligence” and data communities of interest. Blogs are public and long-mining programs. That may be closer than any of lived, and they weave themselves into closeuser care to realize. relationships with other blogs. As such, they may serve as a tool for reflection, knowledge building,1.4 Web Mining and sharing. Web Mining aims to discover theinformative knowledge from massive data sources In this diagram explained, 56% percent sayavailable on the Web by using data mining or blogs are about self-expression. And most post theirmachine learning approaches. Different from personal life. And almost 2/3 of blogs read areconventional data mining techniques, in which data personal diaries.models are usually in homogeneous and structuredforms, Web mining approaches, instead, handlesemi-structured or heterogeneous datarepresentations, such as textual, hyperlink structureand usage information, to discover “nuggets” toimprove the quality of services offered by variousWeb applications. Such applications cover a widerange of topics, including retrieving the desirableand related Web contents, mining and analyzingWeb communities, user profiling, and customizingWeb presentation according to users preference and Diagram 1: Blogs vs Social networkso on. Blogs differ from traditional marketing Web communities could be modeled as media, however, because they allow you to makeWeb page groups, Web user clusters and co-clusters high trust impressions with a valuable demographicof Web pages and users. Web community at a low cost.construction is realized via various approaches on Blogs = High Trust, Moderate Reach and HighWeb textual, linkage, usage, semantic or ontology- Measurability at a Low Costbased analysis. Recently the research of SocialNetwork Analysis in the Web has become a newlyactive topic due to the prevalence of Web 2.0technologies, which results in an inter-disciplinaryresearch area of Social Networking. Social 1749 | P a g e
  3. 3. S.Geetha, K.Sathiya Kumari / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1748-1755 Trust Reach Measurabi Cost of blogs as a social medium. Using the HTML links lity embedded in blog posts from a large data set of blog feeds, we extract the implicit social relationships Blog    $$ between blogs and construct the blog graph (Kumar  et al. 2003). This allows us to examine dynamic E-mail    $ interactions between bloggers. Goal of this paper newslett understand how a specific content propagates in the er blog graph and do the spreading characteristics differ when comparing a video of a recent political Press    $$ event, against a music video. This paper focuses the Release  two aspects of blogosphere. (1) Link structure and Televisi    $ $ $ $ the content sharing trends across multiple domains on Ad  $ and language groups. (2) Posting of you tube links Radio    $$$$ to determine the topics of popular videos and their Ad  spreading patterns in the blog graph. Newspa    $$$$ per Ad  The paper of social media like political blogs can be done by topic modeling for documentTable 1: Comparison of blog advantage collections and studies of social media like political blogs. Apply the linkPLSA-LDA model to a blog1.6 Link prediction in social network corpus to analyze its cross citation structure via Link prediction for social network data is a hyperlinks. The data within blog conversations,fundamental data-mining task in various application focusing on comments left by a comment left by adomains, including social network analysis, blog community in response to a blogger‟s post. Ininformation retrieval, recommendation systems, 2008 Nallapati and Cohen introduced therecord linkage, marketing and bioinformatics. Social LinkPLSA-LDA model, in which the contents of thenetworks are interact with people in a group or citing document and the “influences” on thecommunity and can be visualized as graphs, where a document, as well as the contents of the citedvertex corresponds to a person in some group and an document, are modeled together.edge represents some form of associations are In 2003 Kumar et al., focused on the evolution ofusually driven by mutual interests that are intrinsic the link structure in blogs over several years andto a group. Link predictions a sub-field of social proposed tools and models to study the communitiesnetwork analysis. It is concerned with the problem formed by blogs. They called the graph defined byof predicting the (future) existence of links amongst links between the blog graphs. During in 2007 Shi,nodes in a social network. The link prediction Tseng, and Adamic 2007 focused in compared theproblem is interesting in that it investigates the structure of the blog graph, using multiple snapshotsrelationship between objects, while traditional data in time, against of the web and social networks.mining tasks focus on object themselves. Aspect of the spread of media content through the blogosphere focused the link structure and the2. RELEATED WORK content sharing trends across multiple domains and In this section introduced some relative language groups and examines the posting of Youworks on social networks. According to that related Tube links to determine the topics of popular videoswork in, Rendition behavior in blogosphere (Nardi and their perceptible patterns in the blog graph.et al) conducted audio taped ethnographicinterviews with 23 bloggers, with analysis of their In 2006 Schaller et al., has examinedblog posts. The motivations of blogging are metadata such as gender and age in blogger.comenumerated. Bloggers write blogs to (1) update bloggers. They examine bloggers based on their ageothers on activities and where about, (2) express at the time of experiment, whether in the 10‟s, 20‟sopinions to influence others, (3) seek others or 30‟s age bracket. They identify interestingopinions and feedback, (4) think by writing, and (5) changes in content and style features acrossrelease emotional tension. Nardi et al. research leads categories, in which they include blogging words,to speculate that blogging is much about reading as all defined by the Linguistic inquiry and Wordwriting, and as much about listening as talking. We Count (LIWC) by Penne baker et al., 2007. They didspecifically examined the production of blogs, but not use characteristics of online demeanor (e.g.,feature research will address blog readers and to friends). Their work shows that ease ofassess the relations between blog writers and blog classification is dependent in part on what divisionreaders precisely. is made between age groups and in turn motivates our decision to study whether the creation of social The spread of media ecstatic over with the media technologies can be used to find the dividingblogosphere: In this paper study the trends in the use line(s). 1750 | P a g e
  4. 4. S.Geetha, K.Sathiya Kumari / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1748-1755 Predicting response to political; blog posts withIn predicting the political sentiment of web log posts topic with topic models (Nallpati and Cohen, 1998)using supervised machine learning techniques introduced the Link-PLSA-LDA model, in whichcoupled with feature selection, both Hearst and Sack the contents of the citing document and thecategorized the sentiment of entire documents based “influences” on the documents are modeledon cognitive linguistics models. Other researchers together. This study aimed to model the data withinsuch as Huettner and Subasic, Das and Chen, and blog conversations, focusing on comments left by aTong manually or semi-manually constructed a blog community in response to a blogger‟s post.discriminate word lexicon help categorize the Marlow (2008) using epidemiological terminology,sentiment of a passage. the tools and goals of describing true disease are frequently different than our own. The goal of Pang, Lee, and Vaithyanathan have epidemics research is frequently different tosuccessfully applied standard machine learning determine how far and how quickly an infection willtechniques to a database of movie reviews. They spread rather than to track the disease through thechose to apply Navie Bays, Maximum Entropy and population.Support Vector Machines to a domain specificcorpus of movie formats, the simplest being a 3. METHODOLOGYunigram representation. Hatzivassiloglou and Number of methodology can be used toMcKeown, and Turney and Littman chose to link prediction, ranking and network analysis inclassify the orientation of words rather than a total blogs. Blog post hold individual‟s perception overpassage. They used the semantic orientation of the particular issues. Libe-Nowell and Klienbergcontaining passage. They pre-selected a set of seed propose a link prediction using machine-learningwords or applied linguistic heuristics in order to approach. Here the network, the task is to predictclassify the sentiment of a passage. whether a link exists or not. If we can predict RR relations hold between two web logs from their The collections of data for the sake of social relations using publicly available data.performing data/text mining experiments have along history. In the area of text mining specifically 3.1 Adoption bearing in the blogospherefor blog mining task the first attempt to form a Four social networks among web logscollection of blog‟s dataset was made by, this (Citation, Blog roll, Comment, and Track back).collection was used for TREC 200 blog-track which These relations called social relations because thewas introduced very first time in TREC conference relations are publicly observable and thereforein the same year. Blogs-web log is an online user‟s involve some degree of social consciousness anddiary like application. The political blog-post manifestation. The readership relations and analyzeusually has a clear-cut political bias. The social the user data.networks community has also explored issues ofinterface and diffusion, most frequently using 3.1.1 Behavioral Relationstructural and node properties. As this data is mostly First define the bearing relations asstatic, we are unware of research using timing relations that are observable only from the user log.information for prediction. Bearing relation includes the readership relations between two weblogs, direct messaging, invitation, Web log data mining referred by and so on. In order to provide the reading behavior,MacDougall (2007). That referred to literally first one is analyze the how often a readershipmillions of blogs on the web, with a new one created relation exists when other social relations exist.every 7-second. Because they store their content Then consider the probability of reading relationselectronically in a highly accessible form, blogs are against the number of common neighbors betweenripe for web mining. Data mining operates on both two bloggers on a social network. Finally view thenumerical data and symbolic data, such as text. Text effect of distance on a social network to themining provides a means to wade through the readership relation. For example, if there are 10massive glut of prose that is generated by our bloggers who receive/make comments from bloggercomputer-driven society. Chau and Xu (2007) A and B, the probability of blogger A reading blogreported an application identifying hate groups and B is about 50%. Blog roll and citation relationsracists through network analysis and visualization. include users to read because they create a hyperlinkData sources include HTML documents and link that easily guides a user to the other blog.information. Web log mining falls within thiscategory, along with website visitor profile analysis. 3.1.2 Regularly Reading WeblogsYen (2003) presented an approach of patternanalysis of blog linkages. 1751 | P a g e
  5. 5. S.Geetha, K.Sathiya Kumari / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1748-1755This way used for, how often a blogger reads other role in on line social networks analysis. Wed usageblogs when logged in to the system. The interval of mining is also a tool for measuring centrality degree.reading behavior follows the Poisson distribution. The closeness of blog users can be measured by:Presume that a user reads another weblog  times onaverage in the unit time length. Then the distribution Closeness = (f*(w*b))+(f*(w*r))+(f*w*I))of the interval follows an exponential distribution f(t) =e-t.  is dependent on the weblog; a user might In the equation above, f denotes theoften read interesting weblogs while others do not frequency of a blog behavior, and w is the weight ofdo so very often. closeness for each blog behavior. The three blog behaviors are b=browsing, r=reading andUser Both have cats as pets. User 9535 often I=interaction. This is just a simple example of web9535 and sends a comment to the entries of User usage mining, but the techniques allow manyUser 12084. They upload photographs of possible means of on line social networks analysis.12804 their cats and comment that “This pose Web structure mining is the third kind of web is very pretty”, “Cats are fun to watch. mining and it is also useful for extracting and They are always mysterious”. and so constructing on line social networks to extract the on. links from WWW,, e-mail or other sources. It canUser They are friend‟s offline, and often also be used to analyze path length, reachability or10365 exchange comments. “Followed your to find structural holes, which are very basic andand advice, and painted my nails pink”, traditional social networks analysis.User “I‟m jealous. I want a denim skirt,5461 tool!” and so on. 3.2.1 Web mining process in online socialUser They often write about music. User network analysis25027 27145 put an entry “Please tell me yourand favorite songs related to the moon orUser stars”. , and User 25027 post an entry in27145 reply “My memorable songs related to the moon or stars” with sending” with sending a track back.Table 2: Typical examples of RR relations Machine learning approach is used todefine information diffusion on regular readingchannels between two blogs. Using these relationsfor detecting diffusion: readership relations. Twocases include here: (1) How likely is information todiffuse between two blogs with and without RRrelations? (2) What kind of information is likely todiffuse through RR relations? In both cases,includes a url in an entry is analyzed. Here,information diffusion on RR relations havefollowing possibilities: Easily identifiable, butinformation is rarely represented in the form of urls; Diagram 2: Process of web mining in online socialusers do not mention the url in their entries even if network analysisthe information of the url is propagated: theinformation might propagate from other over a long First select the analysis targets, such asdistance, or media other than weblogs, which causes web, email, telephone communications, etc.coincidental detection of information propagation. Sometimes, more than one target will be selected. After this step, select what kind on line social networks analysis for proceed with. The analysis3.2 Web Mining Techniques For Online Social targets and on line social networks have beenNetworks selected, then next one is data preparation. Here data There is three web-mining techniques are will be collected for analysis, then cleaned andusing in online social network analysis: (1) Web formed as the final format to store in database.content mining, (2) Web usage mining (3) Web Selecting the web mining techniques to be used andstructure mining. Web content mining can also be then proceeding with them. More than oneused in on line social networks analysis ro analyze technique may be selected and sometimes ausers reading interests, and determine their favorite combination of techniques is necessary. Thecontent. Web usage mining also plays an important 1752 | P a g e
  6. 6. S.Geetha, K.Sathiya Kumari / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1748-1755selected techniques and then used to analyze the granularity is too fine, the algorithm is verydata collected and prepared in the third step of the perceptive to noise and outliers. Graph clusteringprocess. Visualization techniques are sometimes algorithm based on the “edge betweens” of an edgeused to assist the presentation of the results of the in a graph. The “betweens” of an edge is defined asanalysis, such as the extracted social networks. The the number of shortest paths between all pairs oflast one is general process to use web mining for on vertices that run along the edge. By removing theline social networks analysis is recommendation and edge with the highest “betweens”, the graph will beaction. partitioned. Generating a named entity graph, need a collection of documents regarding a certain named3.3 Tacit edifice with Page Rank of Blogspace entity and then extract all the sentences containing In produce the tacit link structure is became more than one entity‟s name, which is a personobvious that these new graphs differed from the name in this case. To behold named entity tenuresexplicit structure of blogspace. These differences from the documents, here using MINIPAR as themay alter the results of ranking algorithms that have named entity parser. All the sentences parsed bylargely depended upon the explicit link structure MINIPAR, only those sentences containing more(e.g., www.technorati.com) or have treated blogs in than one name are collected. Then map the selectedmuch the same way as regular web pages (e.g., sentences into a weighted undirected graph.Google‟s Page Rank). Well read blog may acquireinformation from a less popular source may miss out Google Wed search and Google Blogon blogs that initially spread infection. To conquer search used by “Tom Cruise” as the blog querythis problem utilizes the timing techniques described because of this is popularity among bloggers. Theabove to infer implicit links between blogs and rank blog documents were collected manually becausethe blogs according to those implicit links. For the most of blog sites have finally provide API for userspurpose of ranking blogs do not attempt to infer the to access their collections. Set of documentsmost likely infection link but rather all possible retrieved over with Google Web search engine. Thisinfection routes. Page Rank trends to assign high selection is based on except Web documents have aratings to the sites that contain original materials. broad coverage on a given person entity and assumeiRank tends to assign high ratings to the sites that that the retrieved top-ranking documents should beserve as community portals, such as lists of popular of high quality. Because there are millions of pagesURLs, lists of important blogs, and discussion relevant to our query entities.boards. Another difference is in the way Page rankand iRank treat duplicate blogs. 3.3.3 Link Induction and visualization The first classifier (svm) predicted three3.3.1 Classification different classes (restitute links, one way links, and Two SVM models using the free LIBSVM un between linked (undirected) and unlinked pairs.toolkit, with a standard radial basis function. The linked pairs). A second classifier (both implementedfirst classifier predicted three classes (reciprocated in SVM and as a logistic regression) imposinglinks, one way links, and unlinked pairs). A second simply between linked (undirected) and unlinkedclassifier, which used in the final application, pairs. These classifiers using a set of heuristics nowdistinguish simply between linked (undirected) and able to construct an infection tree, with each nodeunlinked pairs. All classifiers were trained with 10- representing a blog. Links in the tree between thefold cross validation. Graph inference is achieved nodes show how infection may have spread for athrough the use of the classifier. For all blogs that specific URL. The system automatically producesare not connected to another blog at an earlier date figures representing the specific link structure. Thethe classifier proposes links. Graphs are generated current system only holds edges deduce through theusing the Graphviz tool, which allows for easy two-class link deduce SVM.creation of timeline style figures. The coordinatesdetermined by Graphviz are used to render the graph 3.3.4 Content sharingin Zoomgraph, a Java based tool developed to Here examine three aspects of contentvisualize and explore graph structures. Users can sharing patterns in the blogosphere. (1) What are theuse the Zzoomgraph applet to control the threshold topics and categories of videos that are popular, (2)for the display of links as well as the types of links what is the age of the shared videos, (3) how quicklythat should be displayed. do links to the same video spread in the blogosphere? The number of HTML links to You3.3.2 Clustering graph into communities Tube videos in the blogosphere follows Zipf‟s law. Variety of techniques on graph clustering This hints us at the existence of a large-scalehas been used. HITS to discover communities. It is diffusion of You Tube videos. The median values ofvery difficult to set the eigen value threshold to the half times and full times of videos for the 4select the communities. If the granularity is course, video categories. Recall that the Spinner3r imprintall communities will merge into one. If the spans only two months, Spinner3r‟s web crawler 1753 | P a g e
  7. 7. S.Geetha, K.Sathiya Kumari / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1748-1755can discover posts that much older than two months Figure 2: The four epidemic expression profilesfor blogs that published posts rarely. resulting from the k-means clustering procedure: (a) 124 of 259 URL time course profiles are of the sustained interest type, (b) 51 URLs peak on day two followed by a slow decay, (c) 38 URLs peak on day one with a very fast decay, and (c) 46 URLs have a peak on day one with a slower decay. For the analysis 259 URLs selected from all the URLs mentioned by blogs between the period of May 2, 2003 and May 21, 2003 according to the following criteria: (i) the URL was cited at least 40 times, (ii) the URL was not on a stop list os 198 URLs (e.g. the homepages of common news sources such as moveabletype.org), and (iii) the URL wasFigure 1: Time lag in the spread of videos in the not mentioned by any blogs on May 2, 2003. Thisblogosphere tended to eliminate profiles that might have their peak before May 2, but do not sustain interest past3.4 Link structure of the web pages that point. Spreading patterns can greatly us aboutgeneral information flow in networks. However, to 4 CONCLUSIONunderstand how a specific URL has spread requires This study has examined the interactiona closer look at the network structure. Blog creators between social relations and behavioral relations.frequently provide a list of other blogs frequently Several issues require further analysis, but weread by the author or automated track backs. URLs believe that we have shown a comprehensivethat are reputably cited within the community of overview of social relations that can be associatedbloggers are tracked by several websites. The with readership relations and addressed the problempopularity measurements of these URLs act as a of link prediction in networked data. The novelsimple filtering and ranking mechanism, allowing model can collectively capture globally predictiveusers to quickly find potentially interesting URLs intrinsic properties of objects and discover the latentthat many bloggers are talking about in near real- block structure, which shows the success of thetime. coupled benefits feature based approaches and latent block models. The propagation information through blogspace, both to uncover general trends and to explain specific instances of URL transmission. The success of the simple inference, we would like to augment the simple weighting scheme for the iRank algorithm to include the SVM to calculate from another. Both the availability and quantity of time resolved information is unique to blog data. Using it we were able to not only infer link structure, but also to create a novel-ranking algorithm. iRank for ranking blogs. Several probabilistic topic models are applied to discourse within political blogs. A novel comment prediction task to assess these models in an objective evaluation with possible practical applications. The results show that predicting political discourse behavior is challenging, in part because of considerable variation in user behavior across different blog sites. The problem of mining communities from web pages and blogs by exploiting the named entity co-occurrence. Mapped web documents into a named entity graph. An effective hierarchical clustering algorithm, which utilities both the triangle geometry inside a graph and the mutual information 1754 | P a g e
  8. 8. S.Geetha, K.Sathiya Kumari / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1748-1755between vertices. The community quality is [6] Dredze, M. Crammer, K, and Pereira, F.evaluated by the summery terms. Based on the (2008) Confidence weighted linearevaluation, we believe that the technique can classification. In proceedings of the 25thenhance our ability to acquire knowledge from web international conference on machinepages and blogs. learning, July 5-9, Helsinki, Finland. [7] Gordon, A., Cao, Q., & Swanson, R. Web log mining usually involves mining of (2007) Automated Story Capture Fromtext. Text mining is much more challenging than Internet Weblogs. Proceedings of thequantitative data mining. Numbers are much easier Fourth International Conference onto process by computer than text. However, many Knowledge Capture, October 28-31, 2007,tools have been developed to find patterns in text Whistler, Bc.that lead to human knowledge. This can include [8] Kleinberg, J. Authoritative sources in alearning about the intentions of competitors, as well hyper linked environment, Journal ofas study of general customer behavior. Web mining ACM, 46(5). 604 632, 1999.is a natural extension of the use of technology. The [9] P. Kolari, A. Java, and T. Finin.vast majority of website content is predominately Characterizing the splogosphere. In Procnot of interest to any one individual. Web mining 3rd Annual Workshop on Webloggingprovides more efficient ability to identify relevant Ecosystem, 2006.content. Along with this technological ability, [10] Leskovec, J.; Krause, A.; Guestrin, C.,comes the risk of abuse. Faloutsos, C.; VanBriesen, J., and Glance, N. 2007. Cost-effective Outbreak Detection Page Rank algorithm remains complex and in Networks. In ACMSIGKDD.quite badly known, maybe because its authors keep [11] Y. Lin, H.Sundaram, Y. Chi, J. Tatemura,it secret for industrial security obvious reasons. and B. Tseng Discovery of blogThus, even though our explanations can not be communities based on mutual awareness.100% reliable, they reflect the experiences of In WWW2006 Workshop on Weblogginghundreds of users. Another important point to notice Ecosystem, 2006.in this conclusion is that Page Rank is only one of [12] A. Menon and C. Elkan. Link predictionthe criteria involved in the Google search algorithm: via matrix factorization ECML-PKDD,having a good Page Rank isnt up to have very good 2011.rankings! Our recommendation is to pass time [13] NILTE Blog Census,creating rich content for your visitors, because its http://www.blogcensus.net/the actual value-added of your site. Read our [14] Noble, C. and Cook, D. 2003. Graph-basedadvices for optimizing your links (anchor texts are anomaly detection. In KDD-03, 631-636.very important), choosing your titles and your meta Washington, Dc, USA.tags. Related inference techniques to produce novel- [15] Owslev, S., Hammond, K., Shamma, D.,ranking algorithms for blog search tools and the Sood S.(2006) Buzz: Telling Compellingstudy of meme “mutations”. Stories. ACM Multimedia, Interactive Arts program, Santa Barbara, CA.REFERENCES [16] Pastor-Satorras, R. and A. Vesignani, [1] L. Adamic and N. Glance. The political Epidemic spreading in scale-free networks, blogosphere and the 2004 U.S. election: Physical Review Letters, 86(2001), pp. Divided they blog. In linkKDD-2005, 3200-3203. 2005. [17] Robertson, S. Spark-Jones. K, Relevance [2] Adamic, L.A., O. Buyukkokten and E. Weighting of Search Terms, Journal of Adar, A social network caught in the web, American Society for Information Science, First Monday, 8(6). 27, 1988. [3] M. Airoldi, D. Blei, S. Fienberg, and [18] Shi X.; Tseng. B.; and Adamic, L. A. 2007. E.Xing, Mixed membership stochastic Looking at the Blogosphere Topology block models. JMLR, pages 1981-2014, through Different Lenses. In ICWSM. 2008. [19] I. Titory and R. McDonald, 2008. A joint [4] Dearstyne, B.W. (2005) „Blogs: the new model of text and aspect ratings for information revolution?‟ The Information sentiment summarization. In proceedings Management Journal, Vol. 39, No.5, pp.38- of ACL-08: HLT. 44. [20] Zhou, D., Ji, X., Zha H., Giles, L., Topic [5] Domingos, P., and M. Richardson, Mining Evolution and Social Interactions: How the Network Value of Customers, KDD‟01: Authors Effect Research, in the Knowledge and Data Discovery, San proceedings of the 15th ACM CKIM, 2006. Francisco, CA, 2001. 1755 | P a g e

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