Tomographic Clustering To Visualize Blog Communities as Mountain Views

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1 Tomographic Clusering To Visualize Blog Communiies as Mounain Views Belle L. Tseng NEC Laboraories America N. Wolfe Road, SW3-350 Cuperino, CA USA Junichi Taemura NEC Laboraories America N. Wolfe Road, SW3-350 Cuperino, CA USA Yi Wu Universiy of California, Sana Barbara Dep. of Elec. and Comp. Engineering Sana Barbara, CA ABSTRACT Blogs have creaed a fas growing social nework on he Inerne. However ranking soluions are no sufficien o capure relaionships beween imporan blogs and beween communiies. In our paper, we combine blog rankings wih heir social connecions o provide a framework o undersand muliple blog communiies. A novel mounain view visualizaion is provided o explore differen communiies of ineres in blogspace. The mounain views are generaed using a omographic clusering algorihm on he blog social nework. The mounain view shows mounains of communiies consising of conneced blogs. Peaks and valleys of he mounain view depic represenaive blogs as communiy auhoriies and communiy connecors, respecively. We developed a rerieval and exploraory sysem o illusrae his framework, and perform iniial experimens o validae he resuls. Caegories and Subec Descripors H.3.3 [Informaion Sysems]: Informaion Search and Rerieval; H.3.5 [Informaion Sysems]: Online Informaion Services; H.5.4 [Informaion Sysems]: Hyperex/Hypermedia Keywords Blogs, communiy, informaion disseminaion, influence, auhoriy, ranking, clusering, graph conneciviy. 1. INTRODUCTION Recenly, blogs (or weblogs have become prominen social media on he Inerne ha enable users o quickly and easily publish conen including highly personal houghs. A blog is ypically a web sie ha consiss of daed enries in reverse chronological order wrien and mainained by a user (blogger using a specialized ool. Since a blog enry can have hyperlinks o web pages or oher blog enries, he informaion srucure of blogs and links (someimes called he blogspace can be seen as a nework of muliple communiies. By visualizing he blogspace as a social nework of bloggers, various echniques provided by he social nework research communiy can be inroduced, for example, how blogs communicae wih each oher. In addiion o radiional analyses, however, we mus consider special characerisics of he blogspace compared o ypical social neworks in he real world: dynamics and divergence of populariy and qualiy of blogs. Copyrigh is held by he auhor/owner(s. WWW 2005, May , 2005, Chiba, Japan. Alhough here is no predefined hierarchical srucure in he blogspace (unlike mass media, a blog communiy is no fla. Compared o he real world nework, one can become much more easily known o ohers in he blogspace. For insance, a blog ha provides ineresing enries can arac worldwide audiences and ge ciaion links from ohers. In his manner, a celebriy wihin an ineres communiy can emerge dynamically. On he oher hand, since anyone can publish and link o any blog, here are many unimporan or even harmful enries such as spam. Linkbased rankings such as PageRank [9] are useful o deec popular blogs. To analyze communiy srucure, however, a simple ranked lis does no provide enough informaion. Insead populariy (given by ranking score is incorporaed ino he connecions beween blogs, and we inroduce he concep of ranking-based conneciviy. Our focus is no on he enire nework of blogs bu on a social nework of imporan blogs (i.e., blogs aking imporan roles in a communiy and rused by he communiy members. Useful and rused discussions can be exraced from such a nework. If wo celebriies have a common opic of ineres, i is naural ha hey are aware of each oher and likely o sar communicaion (publishing enries referring o each oher and produce valuable discussions referred o by he communiy audiences. This srucure of a blog communiy can emerge everywhere in he blogspace. Even in a paricular opic, here can be muliple diverse communiies. Our goal is o capure he communiy landscape on a specific opic and allow users o explore hese imporan blog communiies. In his paper, we propose (1 mounain view, a new visualizaion echnique ha provides a landscape of blog communiies in erms of populariy and conneciviy, (2 omographic clusering, he underlying algorihm ha generaes a mounain view from a social nework of blogs wih ranking score informaion, and (3 archiecure for communiy rerieval and exploraion sysem based on he algorihm and visualizaion. Our sysem allows he user o specify a query and rerieve a mounain view of he opic, hrough which he user can explore various communiies. The paper is organized as follows. Secion 2 reviews relaed work on blogspace and communiy exracion. In Secion 3, we propose our sysem and algorihm o help a user undersand he communiy srucure of blogs on a specified opic. Experimenal seup for daa collecion and preliminary resuls on he daa are shown in Secion 4 and Secion 5, respecively. Secion 6 summarizes our conclusions and fuure works.

2 2. RELATED WORK Recen research on he blogspace focuses on wo maor aspecs: emporal analysis and informaion diffusion among blogs. For emporal, Kumar proposed a mehod o idenify bursy communiy of blogs based on communiy exracion and burs analysis [6]. Adar inroduced he Epidemic Profile o characerize he emporal behavior of posing enries ha refer o specific URLs [1]. BlogPulse is a sysem ha provides rend graphs ha shows he populariy of specific opics over ime [3]. Gruhl analyzed emporal characerisics of blog posings on a specific opic [4]. To sudy informaion diffusion, Adar proposed a mehod o find implici informaion propagaed beween blog enries and presened irank, a ranking algorihm based on he implici link srucure [1]. Gruhl inroduced a probabilisic model of informaion propagaion among individuals and proposed an algorihm o induce a ransmission graph ha capures informaion diffusion srucure in he blogspace [4]. Our focus is, given he explici blog influence as a nework, o undersand he underlying communiy srucures. Our framework incorporaes various resuls from graph-based informaion diffusion. There have also been exensive sudies done on idenifying communiies from he link srucure of he Web. The HITS algorihm idenifies hub and auhoriy web pages, where a hub links o many auhoriies and an auhoriy is linked by many hubs [5]. In his algorihm, a communiy is modeled as a biparie graph of hubs and auhoriies. The rawling algorihm [7] has been proposed o discover such communiies (i.e., dense biparie subgraphs from a huge daa se. Flake defines a communiy on he web as a se of sies ha have more links o members of he communiy han o non-members, and models he graph pariion based on maximum flow and minimal cus [2]. The communiy char algorihm [10] idenifies muliple communiies and relaionships beween communiies. Communiies are idenified by pariioning Symmeric Derivaion Graph whose link represens relaionship beween wo sies such ha each sie is derived as a op-n auhoriy from he oher sie. In his paper, our focus is no on exracing subgraphs of general link srucure bu on undersanding he graph srucure of ranked nodes. 3. COMMUNITY UNDERSTANDING A key challenge o explore he blogspace is o undersand he differen blog communiies and idenify imporan represenaives. In his secion, our obecive is o allow users o ineracively undersand he blogspace by providing a sysem framework for rerieving relevan communiies and ineracively explore he auhoriaive and allied blogs. 3.1 Sysem Overview The blogspace is growing and becoming more difficul o idenify imporan blogs for a user s opic of ineres. We propose a sysem framework ha will allow a user o specify a query, rerieve relevan communiies, and ineracively explore he communiies by examining he exraced represenaive blogs. To achieve his obecive, our sysem is composed of wo maor componens, (1 Communiy Rerieval and (2 Communiy Exploraion. Figure 1 illusraes he wo modules. In Communiy Rerieval, he module allows he user o specify a opic of ineres and reurns he highly-relevan clusering of communiies, which we refer o as he Mounain View of his query. When a query is given, he Enry Rerieval componen rerieves relevan enries from he blogspace. These relevan enries allow he Blog Ranking componen o rank he blogs, as will be described in he following subsecion 3.2. Following, Tomographic Clusering is performed o discover communiy clusers of conneced and relevan blogs, as explained in subsecion 3.3. The clusering resul generaes a mounain view represenaion of he discovered communiies corresponding o he user query. In Communiy Exploraion, he module akes he reurned mounain view of a query and provides he user wih a visual exploraion of he relevan communiies and represenaive blogs. The user can ge a high-level view (Ala Visa of he rerieved communiies in his query space by observing he visualizaion. In addiion, he user may be ineresed in examining he opranked communiy or alliances beween communiies. Furhermore, represenaive blogs can be exraced o represen he complex communiy landscape. Figure 1. Sysem Overview of Communiy Rerieval and Communiy Exploraion o Undersand Blog Communiies. In our rerieval and exploraion sysem, he user specifies a query and ineracively examines ineresing communiies wih various levels of deails via he mounain view represenaion. One applicaion includes a blog opic summarizer, where he user idenifies a opic and he sysem shows a lis of represenaive blogs exraced from each of he op-n ranked communiies. This allows he user o quickly ge an overview of he differen communiies in his opic. Furhermore, our sysem provides a framework for oher applicaions o be build based on he derived mounain view represenaion. 3.2 Blog Ranking To locae valuable web conens on he Inerne, search engines are used o rerieve a rank ordering of imporan pages wih respec o he user s reques. Similarly wih web log conens, blogs can be rerieved and ranked. Our goal in his subsecion is o illusrae one mehod o rank he blogs. When a user idenifies a query of ineres, he rerieval module needs o find relevan enries before blogs can be ranked. The seps are as follows. Firs, relevan enries are idenified. In our sysem, an enry is labeled relevan wih respec o he query if he query erm exiss in he enry. Second, he impac scores of relevan enries are calculaed. Finally, he ranking scores for he blogs are derived from he enry scores. The impac of enries may be calculaed by an ieraive procedure much similar o he PageRank algorihm used in he Google search engine. Le enry graph EG be denoed by he se of enries E = {e i and se of enry links EL = {(e i, e where enry e i cies enry e, hus EG = (E, EL. Given an enry graph EG, he score of an enry s(e can be calculaed by he following ieraive Equaion 1.

3 s( e s ( ei = (1 d + d Eq. (1 e IN( ei OUT( e Here IN ( e i represens he se of enries ha cies e i, IN( ei = { e ( e, ei EL, and OUT ( e i represens he se of enries ha e i cies, OUT( ei = { e ( ei, e EL. Consequenly, OUT ( e i represens he oal number of enries ha e i cies. Finally, d is a parameer o conrol he damping facor o he rank propagaion. Nex we consider a se of blogs B = {b i. Each blog b i owns a se of enries E i. There can be muliple enry-o-enry links from blog b i o blog b, which we denoe as enry link ELi = {( ek, el ek Ei, el E,( ek, el EL. Thus he social nework of blogs B can be represened as a graph G = (B, L where he se of links L = {( bi, b ELi φ. There are various ways o score blogs based on he enry scores. Currenly, we ake he following approach o calculae he ranking score s(b of blog b, as shown below in Equaion (2. s( b = i 1 b ( IN bi ( k, l EL e e EL i i s( ek Eq. (2 Here IN(b i represens he se of blogs ha have enries ciing o blog b i, IN( bi = { b ( b, bi L. We incorporae he normalizaion EL o refer o he oal number of enry links i from blog b i o blog b. As a noe, we have ried oher blog ranking mehods like Equaions (3 and (4, 1 s ( bi = s( e Eq. (3 Ei e E i where he blog score is he average of he blog s enry scores, and s ( b = s( Eq. (4 i e e IN ( b i where he blog score is he sum of scores from ciaion enries. However hese do no resolve cerain deficiency ha we observed. There are wo underlying reasons we chose Equaion (2 o calculae our blog ranking scores. Firs, we observed ha here was a high variance in he oal number of enries ha a blog can own, variance of E i is large. We find ha some blogs have a large number of enries and mos of hese enries are no referred o by oher blogs. In order o focus on blogs ha have mos enries referred o by oher blogs, we needed o derive a score ha accumulaes he effec of references by oher blogs. Second, we observed ha here was a high variance in he oal number of links beween blog b i and blog b, variance of EL i is large. Some blogs have a very large number of links beween each oher (e.g., muliple blogs owned by a single person. Insead of accumulaing he effec of each reference beween wo blogs, we chose he average of hose. As a resul, Equaion (2 seems o capure our desired oucome. For our example query, we chose he opic google. We perform blog rerieval and ranking using Equaion (1 for ranking he enries, followed by Equaion (2 for ranking he blogs. Figure 2 displays he resuls of he op ranking 30 blogs by he red squares and he blog ciaion links beween hem, subsequenly referred o as he blog social nework. The numbers depiced nex o he blogs denoe he blog IDs used for cross-referencing. Also, he heavier he blog links, he sronger heir relaionships are. As eviden from he figure showing he google blog social nework, here is one large srongly-linked communiy and some smaller and less-conneced communiies. In he following subsecion, we explore how o discover and compare hese communiies. 3.3 Communiy Discovery Ranking-Based Conneciviy Analysis The social nework in Figure 2 shows muliple disconneced communiies of op-ranked blogs. However noe ha he conneciviy of hese graphs depends on he hreshold of ranking resuls if anoher blog is added by lowering he hreshold, i may connec muliple communiies ogeher. In fac, he ranking of blogs is imporan when we consider social conneciviy of blogs. Blogs linked by a highly-ranked blog appears more conneced han blogs linked by a lower-ranked blog (for example, he lower ranking blog may be a spam. We call his conneciviy as ranking-based conneciviy. A formal definiion of ranking-based conneciviy is given as follows. Le G = (B, L be a graph ha represens a social nework of blogs, where B is a se of blogs (verices and L = {( bi, b bi, b B is a se of social connecions (edges. G can be represened as a disoin union of conneced subgraphs G = UC, where C i i represens a conneced subgraph. Le B be he op-ranking se of blogs wih hreshold (i.e., B = { b b B, s( b and G be he induced subgraph of G corresponding o B (i.e., he graph afer removing blogs whose scores are under he hreshold. Then given a hreshold, a se of conneced subgraph {C i is given asg U = C i. The rank-based conneciviy rc(b 1, b 2 beween wo blogs b 1 and b 2 is defined as he maximum hreshold such ha i : b1, b2 C i. The criical blog ha connecs muliple graphs ino C i is called he connecing blog of b 1 and b 2, denoed by bc(b 1, b 2. 1 The score of he connecing blog is equal o he rank-based conneciviy, s(bc(b 1, b 2 = rc(b 1, b 2. Ranking-based conneciviy beween wo disoin conneced-subgraphs (i.e., wo blog communiies is defined similarly Tomographic Clusering We are ineresed in reflecing he changing srucure of {C i when varies from he minimum score o he maximum score. Alhough a snapsho can be seen in Figure 2, we wan o capure a concise represenaion ha shows he communiy srucure given he rank-based conneciviy analysis. We propose an algorihm ha provides such a represenaion, called omographic clusering. Tomography is aken from compuer omography (CT scan ha generaes 3D srucure of a human body by accumulaing 2D slices. Given differen Figure 2. Social Nework of Top 30 Blogs for google query. 1 There may be muliple connecing blogs wih an equal score.

4 hresholds of ranking score, differen slices of he communiy srucure is given as a se of clusers. The overall srucure can be undersood hrough accumulaion of muliple slices. Tomographic clusering generaes an ordered sequence of blogs s = (b 1,,b n ha represens he ranking-based conneciviy of blogs in he following manner: When he sequence s is spli ino a se of coniguous subsequences {s i by removing blogs under he hreshold, he se of blogs in each subsequence s i is equal o he se of blogs in C i. Figure 3 illusraes a visual inerpreaion of he clusering resul. A curve called he mounain view is drawn by ploing blogs in he order of he sequence s wih heir scores on he verical axis. If he mounain view is cu a he score hreshold, he se of curves above corresponds o he se of conneced subgraphs {C i. Mounain view can be seen as he conour of he communiy srucure since he curve shows upper bounds of pahs wihin he social nework. For insance, suppose here are blogs b 1, b 2, b 3 in his sequenial order, and s(b 2 is lower han s(b 1 and s(b 3, hen any pah beween b 1 and b 3 mus conain b 2 or oher blogs wih a score lower han b 2. score C 1 blog Figure 3. Mounain view visualizaion o illusrae muliple communiies of ineres Mounain View In he mounain view, any op porion of he mounain cu by a horizonal line can be seen as a communiy. In Figure 3, he hreshold cus he mounain view ino 3 such communiies, C 1, C 2, and C 3. Blogs wihin each communiy are conneced by a ranking-based conneciviy value higher han he hreshold. Given a communiy as a par of he mounains, wo ypes of represenaive blogs can be visually idenified in he mounain view. (1 Communiy auhoriy a(c: The blog wih he highes score in he communiy is visualized as he highes mounain peak. (2 Communiy connecor c(c i,c : The connecing blog beween communiies C i and C is visualized as he lowes poin in he valley beween he C i and C communiies. In he nex subsecion, we will describe he omographic clusering algorihm o idenify hese wo ypes of represenaives Tomographic Clusering Algorihm A sequence of blogs ha saisfies he definiion of omographic clusering exiss if duplicaion of a blog is allowed in he sequence. A sequence can be generaed by racing he clusering process of {C i from he maximum score o he minimum score: insead of merging ses of nodes, he algorihm concaenaes sequences. C 2 C 3 cluser( reurns sequence { Le sequence_se S = 0 For each blog b in B in he ranking order Le sequence_se S c = {s connecs(b,s, s in S If (S c = 0 S = S + {(b If ( S c = 1 le sequence s in S c, S = S S c + {add(s,b If ( S c > 1 S = S S c + {conca(s c,b Reurn conca(s, b0 Saring from he op rank blog, he algorihm adds blogs in he ranking order and mainains a se of sequences as an inermediary resul. For each blog b added, he algorihm checks wheher b is conneced wih any of he curren sequences. If i is no conneced wih any sequence, a new sequence ha conains only b is creaed. If i is conneced wih only one sequence, b is added o ha sequence. If i is conneced wih muliple sequences, hey are concaenaed ino one sequence wih inserion of b beween wo sequences. Blog b0 is an imaginary blog ha connecs all blogs in B, which is inroduced o generae one sequence as he final resul. connecs(b,s reurns a boolean value ha represens wheher he blog b is direcly conneced o any blog in he sequence s. connecs(b:blog, s:sequence reurns boolean { If (exiss b in S such ha (b,b in L or (b,b in L reurn rue Else reurn false add(s,b creaes a sequence by adding blog b o sequence s a eiher he head or ail. In his algorihm, he head and ail is chosen in alernaively for esheic reasons when he sequence is visualized. add(s:sequence, b:blog reurns sequence { If ( s is odd reurn s + (b Else reurn (b + s conca(s,b creaes a sequence by concaenaing sequences in a sequence se S wih insering blog b beween sequences. Sequences can be concaenaed in any arbirary order. conca(s:sequence_se, b:blog reurns sequence { If ( S = 1 reurn s where s in S Else reurn s + (b + conca(s {s, b where s in S 3.4 Communiy Exploraion In his secion we will provide a visual exploraion of communiies using he mounain view. Figure 4 illusraes how he user can explore communiy srucure visualized as a mounain view. The user can exrac a communiy as a porion of he mounain by specifying a cuing line. Given his communiy as he focused communiy, relaed communiies can be exraced as a se of maximal communiies conneced o he focused communiy. In Figure 4, he user specifies he communiy in focus C 0 and acquires, as a resul, a se of relaed communiies C 1,, C 6. The user can ge a rough idea on he imporance and relevance (conneciviy of a relaed communiy (C i from he heigh of he local peak s(a i, he deph of he valley s(c i, and he size of he mounain.

5 Communiy auhoriies ai ha give ranking scores of Ci a 2 a 1 a 5 a3 C C 0 1 a 4 C 2 he cuing line C C C c c 2 c c 4 c 3 5 Figure 4. Mounain view of muliple communiies showing communiy auhoriies and allied connecors. In addiion, he sysem may provide a summary of conen informaion for each communiy (such as keywords. The granulariy of a relaed communiy is given based on is rankbased conneciviy wih he focal communiy C 0 (i.e., he weaker he connecion, he coarser he communiy. This feaure provides he focal informaion wih conexual informaion o navigae he user o various communiies. For example, C 6 is given as a coarse-grained cluser wih respec o C 0 because i is weakly conneced (i.e., he rank-based conneciviy rc(c 6, C 0 = s(c 6 is low. If he user wans o focus on more deailed communiy srucure wihin he communiy C 6, he or she can cu ou a subcommuniy from C 6. By changing he focal poin (i.e., C 0 in his manner, he user can explore muliple communiies in he mounain view. 4. EXPERIMENTAL SETUP For our iniial experimens, we have developed a focused crawler o collec blog daa from he Inerne. The crawling process is divided ino five componens, (1 iniial seeds, (2 blog discovery, (3 enry exracion, (4 seed expansion, and (5keyword rerieval. For iniial seeds, we sar wih a focused lis of opic keywords. Given a keyword, he crawler searches recen enries using public RSS search engines. From he search resul, blogs are discovered as he iniial se of seeds. The crawler rerieves RSS files of he seed blogs and pages referred o by he RSS files. Nex for blog discovery, when a crawled page has an HTML link ag ha refers o an RSS (his common feaure of recen blog ools is called RSS auo discovery, he crawler checks wheher his RSS represens a blog. If an RSS file saisfies he following condiions, he web sie referred o by he RSS as channel is recognized as a blog if: (1 The RSS conains iems referring o pages in he same hos and (2 Each page referred o by he RSS has an HTML link ag ha refers back o he RSS. In enry exracion, he crawler needs o exrac enry daa from web pages since RSS file does no always conain enire enry conen. The crawler exracs he conen of an enry from he corresponding web page using an exracion paern described wih XPah expressions. Alhough he curren version requires manual generaion and regisraion of paerns, we plan o develop auomaed paern generaor similar o he sysem in [8]. When no exracion paern is available, he crawler looks for a conen snippe in he RSS file. Subsequenly, seed expansion is incorporaed o capure possiblyrelevan conens. From enry pages of he seed blogs, he crawler crawls hyperlinks for N hops (currenly N is se o 1, and discovers blogs. From colleced blogs, ones referred o by muliple blogs in he se are chosen for new seeds. a 0 Communiy connecors ci ha connec Ci o C0 Communiy in focus c 6 C 6 a 6 In keyword rerieval, a se of relevan enries are rerieved from he crawled enry daa based on keyword maching. Then enries referred o by any enry in he resul se are also rerieved and added o he resul se. The blog ranking and he omographic clusering are done on his resul se as will be presened in he following secion. 5. PRELIMINARY RESULTS In order o discover and undersand he muliple communiies in our daase, we developed he rerieval and exploraion sysem described in Secion 3.1. I allows he user o submi a opic query and generaes he corresponding blog social nework and he mounain view. We begin wih subsecion 5.1 where he blog ranking resuls are derived, followed by subsecion 5.2 where he discovered communiies can be inspeced by he mounain view. 5.1 Blog Ranking For a query, users are ineresed in finding he op blogs ha cover his opic. We build our blog search inerface o allow users o inpu heir queries and rerieve he oupu ranking resuls. For he inpu query, users can ener (1 keywords, (2 he period of rerieval, and (3 a selecion of eiher enry or blog ranking oupus. Figure 5 illusraes he blog search inerface where we chose he keyword google and he period spanning from January 1 o February 22, Subsequenly, relevan enries are scored and blogs are ranked as according o Secion 3.2. In he blog search inerface, we can selec eiher enry ranking or blog ranking for our oupu. Do hese wo opions give us similar or significanly differen resuls? In Figure 5, we illusrae he blog social neworks derived from boh scoring. On he lef social nework plo, he blogs corresponding o he op ranked enries are shown. On he righ social nework, he blogs corresponding o he op ranked blog scores are shown. In his google example, i is clear ha he social neworks are quie differen in demonsraing communiy conneciviy. Finally by selecing he blog ranking opion, he sysem generaes he lis of blogs in ranking order wih heir corresponding IDs and impac scores, as depiced on he boom view of Figure 5. Figure 5. Blog rerieval and ranking for he opic google. Social nework based on enry ranking (lef graph and social nework based on blog ranking (righ graph.

6 apan.cne.com rank score (Markeing arifac-p info.seesaa.ne nikkeibp ipod (SEO compeiion 0.01 C 3 umeda C 1 C podcasnow bloggers Figure 6. Mounain view of he opic google. Auhoriies are shown as peaks, and connecors are shown as valleys. 5.2 Blog Ranking As demonsraed in he previous subsecion, muliple communiies are rerieved for a query bu i is difficul o discover he significances of hese communiies oher han manually examining each blog. In his secion, we will demonsrae how he mounain view can be used o explore some of he communiies. Coninuing wih our google query, our omographic clusering generaes he mounain view depiced in Figure 6. Here we can see hree maor mounains C 1, C 2, and C 3. In mounain C 1, four ousanding peaks can be seen. Two of hose are news sies, cne and nikkeibp. One is an announcemen blog of a blog hosing sie seesaa (during his period, seesaa announced a new service. One is a personal blog arifac, who is famous in he Japanese blog communiy. These blogs are regarded as auhoriies and provide informaional enries ha communiy members frequenly refer o. The blogs, umeda and podcasnow, connecing hese four peaks are regarded as good hubs. They do no only refer o informaional auhoriies bu also provide addiional values in heir enries since heir high score shows ha heir enries are frequenly referred o. Mounain C 2 is fairly large bu disconneced from he bigges mounain C 1. In fac, i is a communiy playing a SEO (Search Engine Opimizaion compeiion. Given an arificial word, hey compee for ranking in he Google search resul of ha word. In order o ge high ranking scores, hey link o each oher creaing an inra-conneced communiy bu disconneced from ouside communiies. Mounain C 3 is wo blogs on markeing discussing on recen Inerne based markeing (such as adverisemen in Google. 6. CONCLUSION Blogs provide an opporuniy for people o share imporan informaion in a communiy. In our paper, we provide a mounain view visualizaion for users o explore he differen communiies of ineres in blogspace. The mounain views are generaed using a novel omographic clusering algorihm based on ranking-based conneciviy of he blog social nework. Our clusering combines he blog ranking wih he inheren conneciviy of blog conens. As a resul, we capure he landscape of muliple communiies and idenify represenaive auhoriies and connecors. 7. REFERENCES [1] E. Adar, L. Zhang, L. Adamic, and R. Lukose. Implici Srucure and he Dynamics of Blogspace, WWW 2004 Workshop on he Weblogging Ecosysem: Aggregaion, Analysis and Dynamics, New York, May [2] G. Flake, S. Lawrence, and C. Giles. Efficien Idenificaion of Web Communiies, in Proc. of KDD 2000, ACM Press, New York, 2000, pp [3] N. Glance, M. Hurs, and T. Tomokiyo. BlogPulse: Auomaed Trend Discovery for Weblogs, WWW 2004 Workshop on he Weblogging Ecosysem: Aggregaion, Analysis and Dynamics, New York, May [4] D. Gruhl, R. Guha, D. Liben-Nowell, and A. Tomkins. Informaion Diffusion Through Blogspace, WWW 2004, New York, May [5] J. Kleinberg. Auhoriaive sources in a hyperlinked environmen, In Proc. 9h ACM-SIAM Symposium on Discree Algorihms, [6] R. Kumar, J. Novak, P. Raghavan, and A. Tomkins. On he Bursy Evoluion of Blogspace, WWW 2003, Budapes, Hungary, May [7] R. Kumar, P. Raghavan, S. Raagopalan, and A. Tomkins. Trawling he Web for Emerging Cyber-Communiies, WWW 1999, Torono, Canada, May [8] T. Nanno, Y. Suzuki, T. Fuiki, and M. Okumura. Auomaic Collecion and Monioring of Japanese Weblogs, WWW 2004 Workshop on he Weblogging Ecosysem: Aggregaion, Analysis and Dynamics, New York, May [9] L. Page, S. Brin, R. Mowani and T. Winograd. The PageRank Ciaion Ranking: Bringing Order o he Web, Sanford Digial Libraries Working Paper, [10] M. Toyoda and M. Kisuregawa. Creaing a Web communiy char for navigaing relaed communiies, Proceedings of he welfh ACM conference on Hyperex and Hypermedia, Denmark, Augus 2001.