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text-align:center'><span style='font-size:18.0pt;font-family:"Verdana","sans-serif"; mso-fareast-font-family:"Times New Roman"'>Large graph mining: patterns, tools and case studies</span><span style='font-size:10.0pt;font-family:"Verdana","sans-serif"; mso-fareast-font-family:"Times New Roman"'><o:p></o:p></span></p> <h1 align=center style='text-align:center' id="d_7r0"><span style='font-size: 12.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"'>Tutorial proposal for&nbsp;ICDE 2009, Shanghai, China</span><span style='font-family: "Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"'> <o:p></o:p></span></h1> <p class=MsoNormal align=center style='margin:0in;margin-bottom:.0001pt; text-align:center'><i id=temc0><span style='font-family:"Verdana","sans-serif"; mso-fareast-font-family:"Times New Roman"'><a href="http://www.cs.cmu.edu/~christos" title="Christos Faloutsos" id=bp35><span style='color:#810081'>Christos Faloutsos</span></a> and <a href="http://www.cs.cmu.edu/~htong" title="Hanghang Tong" id=sga->Hanghang Tong</a>,<br id=p50e2> <a href="http://www.cs.cmu.edu/" title="Carnegie Mellon Univeristy" id=gm7j>Carnegie Mellon University</a> </span></i><span style='font-size:10.0pt;font-family: "Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"'><o:p></o:p></span></p> <p class=MsoNormal style='margin:0in;margin-bottom:.0001pt'><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'><br id=x2b30> <br style='mso-special-character:line-break' id=iffo0> <![if !supportLineBreakNewLine]><br style='mso-special-character:line-break'> <![endif]><o:p></o:p></span></p> <h1><span style='font-size:13.5pt;font-family:"Verdana","sans-serif"; mso-fareast-font-family:"Times New Roman"'>Abstract</span><span style='font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"'><o:p></o:p></span></h1> <p class=MsoNormal style='margin:0in;margin-bottom:.0001pt'><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>How do graphs look like? How do they evolve over time? How can we find patterns, anomalies and regularities in them? How to find influential nodes in the network? We will present both theoretical results and algorithms as well as case studies on several real applications. Our emphasis is on the intuition behind each method, and on guidelines for the practitioner.<br id=fu-10> <br id=qkui0> The tutorial has the following parts: <b id=qkui1>(a)</b> Statistical properties and models and graph generators of static and evolving networks. <b id=qkui2>(b)</b> Tools for the analysis of static and dynamic graphs, like the Singular Value Decomposition, tensor decomposition for community detection, HITS/PageRank etc. <b id=qkui2>(c) </b>Proximity measurements on graphs, the main ideas to quantify the closeness of two nodes of the graph, fast algorithms to compute the proximity scores, applications of proximity, like CenterPiece subgraphs, pattern match, trend analysis etc. <b id=qkui4>(d)</b> Case studies of how a virus or information or influence spreads through the network, how to&nbsp; find influential bloggers or nodes to target for viral marketing, how to find fraudsters on eBay, how to find communities on graphs.<br id=onfl0> <br id=onfl1> <b id=nszl0>Keywords</b>: Graph mining, linear algebra, SVD, tensors, pageRank<br id=hu.-0> <br id=ggcf0> <b style='mso-bidi-font-weight:normal'>Foils</b> | <a href="part1_patterns.pdf">Part I</a> | <a href="part2_matrix_tensor.pdf">Part II</a> | <a href="part%203_proximities.pdf">Part III</a> | <a href="part4_case_studies.pdf">Part IV</a> |<o:p></o:p></span></p> <h3><span style='font-size:13.5pt;font-family:"Verdana","sans-serif"; mso-fareast-font-family:"Times New Roman"'>Outline - Description of topics</span><span style='font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"'> <o:p></o:p></span></h3> <p id=iidz0><span style='font-size:10.0pt;font-family:"Verdana","sans-serif"'>The goal of the tutorial is to cover the most powerful tools for the analysis of large, real graphs. The tutorial starts old and new patterns that most real graphs obey (small diameter, power laws etc). It continues with powerful, traditional tools from linear algebra (singular value decomposition SVD, eigenvalue analysis); it shows that they form the basis for the extremely successful PageRank and HITS algorithms; and it concludes with more advanced tools, namely, sparse low rank approximations ('CUR' and derivatives).<br id=ya0u0> The next part focuses on proximity of two nodes on a graph, and how to assess it. We describe several measures (electric current, maximum flow, escape probability), we compare them and we focus on the most successful ones and on fast algorithms to compute them.<br id="o5m_0"> The tutorial concludes with several case studies: influence propagation, fraud detection on e-bay, a survey of algorithms for community detection and graph partitioning, and a description of the map/reduce method for the analysis of Tera- and Peta-byte scale graphs. <o:p></o:p></span></p> <p id=iidz1><span style='font-size:10.0pt;font-family:"Verdana","sans-serif"'>&nbsp; <o:p></o:p></span></p> <p id=iidz2><span style='font-size:10.0pt;font-family:"Verdana","sans-serif"'>The proposed format is 3 hours.<o:p></o:p></span></p> <p style='margin-left:.5in;text-indent:-.25in;mso-list:l1 level1 lfo2; tab-stops:list .5in' id=pjss3><![if !supportLists]><span style='font-size:10.0pt; font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-size:10.0pt;font-family:"Verdana","sans-serif"'>Part I: Patterns [0.5h - Faloutsos]<o:p></o:p></span></p> <ul style='margin-top:0in' type=disc id=dl2s0> <ul style='margin-top:0in' type=circle> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level2 lfo2;tab-stops:list 1.0in' id=pjss2><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>patterns on static graphs <o:p></o:p></span></li> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level2 lfo2;tab-stops:list 1.0in' id=pjss2><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>patterns on time-evolving graphs <o:p></o:p></span></li> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level2 lfo2;tab-stops:list 1.0in' id=pjss2><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>patterns on weighted graphs<o:p></o:p></span></li> </ul> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level1 lfo2;tab-stops:list .5in' id=pjss4><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>Part II: Power Tools [1h - Faloutsos]<br style='mso-special-character:line-break' id=q4ea1> <![if !supportLineBreakNewLine]><br style='mso-special-character:line-break'> <![endif]><o:p></o:p></span></li> <ul style='margin-top:0in' type=circle> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level2 lfo2;tab-stops:list 1.0in' id=pjss6><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>SVD/PCA <o:p></o:p></span></li> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level2 lfo2;tab-stops:list 1.0in' id=pjss6><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>HITS, PageRank <o:p></o:p></span></li> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level2 lfo2;tab-stops:list 1.0in' id=pjss6><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>example-based decomposition: CUR, CMD and Colibri <o:p></o:p></span></li> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level2 lfo2;tab-stops:list 1.0in' id=pjss6><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>tensors - concepts and applications<o:p></o:p></span></li> </ul> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level1 lfo2;tab-stops:list .5in' id=pjss6><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>Part III: Proximity [1h - Tong]<br style='mso-special-character: line-break' id=q4ea2> <![if !supportLineBreakNewLine]><br style='mso-special-character:line-break'> <![endif]><o:p></o:p></span></li> <ul style='margin-top:0in' type=circle> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level2 lfo2;tab-stops:list 1.0in' id=pjss6><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>main ideas, properties &amp; generalizations (random walks, escape probabilities, etc) <o:p></o:p></span></li> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level2 lfo2;tab-stops:list 1.0in' id=pjss6><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>fast algorithms <o:p></o:p></span></li> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level2 lfo2;tab-stops:list 1.0in' id=pjss6><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>applications: (center-piece subgraphs, pattern match etc) <o:p></o:p></span></li> </ul> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level1 lfo2;tab-stops:list .5in' id=pjss6><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>Part IV: Case Studies [0.5 hour - Faloutsos]<br style='mso-special-character:line-break' id=t5320> <![if !supportLineBreakNewLine]><br style='mso-special-character:line-break'> <![endif]><o:p></o:p></span></li> <ul style='margin-top:0in' type=circle> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level2 lfo2;tab-stops:list 1.0in' id=pjss6><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>virus/influence propagation <o:p></o:p></span></li> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level2 lfo2;tab-stops:list 1.0in' id=pjss6><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>blog analysis <o:p></o:p></span></li> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level2 lfo2;tab-stops:list 1.0in' id=pjss6><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>eBay fraud detection <o:p></o:p></span></li> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level2 lfo2;tab-stops:list 1.0in' id=pjss6><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>community detection and tracking (co-clustering,&nbsp; cross-associations, graphScope etc) <o:p></o:p></span></li> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level2 lfo2;tab-stops:list 1.0in' id=pjss6><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>tensor for web mining: TOPHITS <o:p></o:p></span></li> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level2 lfo2;tab-stops:list 1.0in' id=pjss6><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>scalability: map/reduce and graph mining <o:p></o:p></span></li> </ul> <li class=MsoNormal style='margin-top:0in;margin-bottom:0in;margin-bottom: .0001pt;mso-list:l1 level1 lfo2;tab-stops:list .5in' id=pjss6><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>Conclusions<o:p></o:p></span></li> </ul> <p class=MsoNormal style='margin:0in;margin-bottom:.0001pt'><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'><br style='mso-special-character:line-break' id=x2b31> <![if !supportLineBreakNewLine]><br style='mso-special-character:line-break'> <![endif]><o:p></o:p></span></p> <h3><span style='font-size:13.5pt;font-family:"Verdana","sans-serif"; mso-fareast-font-family:"Times New Roman"'>Target Audience</span><span style='font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"'><o:p></o:p></span></h3> <p class=MsoNormal style='margin:0in;margin-bottom:.0001pt'><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>The target audience is data management, data mining and machine learning researchers and professionals who work on static or time-evolving graphs and want to know about tools and models when dealing with large network datasets.<br id=xhb51> <b id="n_m11">Prerequisites:</b></span><b><span style='font-size:13.5pt; font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"'> </span></b><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'>Computer science background (B.Sc. or equivalent); familiarity with undergraduate linear algebra (eigenvectors). The tutorial will focus on intuition and examples, carefully introducing only the minimal necessary mathematical tools, and always focusing on practical applications.<br style='mso-special-character:line-break' id="r_bx1"> <![if !supportLineBreakNewLine]><br style='mso-special-character:line-break'> <![endif]><o:p></o:p></span></p> <h3><span style='font-size:13.5pt;font-family:"Verdana","sans-serif"; mso-fareast-font-family:"Times New Roman"'>About the instructors</span><span style='font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"'> <o:p></o:p></span></h3> <table class=MsoTableGrid border=1 cellspacing=0 cellpadding=0 style='border-collapse:collapse;border:none;mso-border-alt:solid white .5pt; mso-border-themecolor:background1;mso-yfti-tbllook:1184;mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-border-insideh:.5pt solid white;mso-border-insideh-themecolor:background1; mso-border-insidev:.5pt solid white;mso-border-insidev-themecolor:background1'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes'> <td width=118 valign=top style='width:88.8pt;border:solid white 1.0pt; mso-border-themecolor:background1;mso-border-alt:solid white .5pt;mso-border-themecolor: background1;padding:0in 5.4pt 0in 5.4pt'> <p style='margin-right:4.5pt' id=zzlc0><!--[if gte vml 1]><v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f"> <v:stroke joinstyle="miter"/> <v:formulas> <v:f eqn="if lineDrawn pixelLineWidth 0"/> <v:f eqn="sum @0 1 0"/> <v:f eqn="sum 0 0 @1"/> <v:f eqn="prod @2 1 2"/> <v:f eqn="prod @3 21600 pixelWidth"/> <v:f eqn="prod @3 21600 pixelHeight"/> <v:f eqn="sum @0 0 1"/> <v:f eqn="prod @6 1 2"/> <v:f eqn="prod @7 21600 pixelWidth"/> <v:f eqn="sum @8 21600 0"/> <v:f eqn="prod @7 21600 pixelHeight"/> <v:f eqn="sum @10 21600 0"/> </v:formulas> <v:path o:extrusionok="f" gradientshapeok="t" o:connecttype="rect"/> <o:lock v:ext="edit" aspectratio="t"/> </v:shapetype><v:shape id="Picture_x0020_2" o:spid="_x0000_s1029" type="#_x0000_t75" alt="photo" style='position:absolute;margin-left:0;margin-top:-.35pt; width:1in;height:81pt;z-index:1;visibility:visible;mso-wrap-style:square; mso-wrap-distance-left:0;mso-wrap-distance-top:0;mso-wrap-distance-right:0; mso-wrap-distance-bottom:0;mso-position-horizontal:left; mso-position-horizontal-relative:text;mso-position-vertical:absolute; mso-position-vertical-relative:line' o:allowoverlap="f"> <v:imagedata src="icde_tutorial_files/image001.jpg" o:title="photo"/> <w:wrap type="square" anchory="line"/> </v:shape><![endif]--><![if !vml]><img width=96 height=108 src="icde_tutorial_files/image002.jpg" align=left alt=photo v:shapes="Picture_x0020_2"><![endif]><b><span style='font-size:10.0pt;font-family:"Verdana","sans-serif"'><o:p></o:p></span></b></p> </td> <td width=697 valign=top style='width:522.8pt;border:solid white 1.0pt; mso-border-themecolor:background1;border-left:none;mso-border-left-alt:solid white .5pt; mso-border-left-themecolor:background1;mso-border-alt:solid white .5pt; mso-border-themecolor:background1;padding:0in 5.4pt 0in 5.4pt'> <p style='margin-right:4.5pt'><b><span style='font-size:10.0pt;font-family: "Verdana","sans-serif"'>Christos Faloutsos</span></b><span style='font-size: 10.0pt;font-family:"Verdana","sans-serif"'> is a Professor at Carnegie Mellon University. He has received the Presidential Young Investigator Award by the National Science Foundation (1989), the Research<br id=lnwp1> Contributions Award in ICDM 2006, twelve ``best paper'' awards, and several teaching awards. He has served as a member of the executive committee of SIGKDD; he has published over 160 refereed articles, 11 book chapters and one monograph. He holds five patents and he has<br id=lnwp2> given over 20 tutorials and 10 invited distinguished lectures. His research interests include data mining for streams and graphs, fractals, database performance, and indexing for multimedia and<br id=lnwp3> bio-informatics data. (Full CV at <a href="http://www.cs.cmu.edu/~christos/webvitae.pdf" target="_blank" title="www.cs.cmu.edu/~christos/webvitae.pdf" id=cgns>www.cs.cmu.edu/~christos/webvitae.pdf</a> )<b><o:p></o:p></b></span></p> </td> </tr> <tr style='mso-yfti-irow:1;mso-yfti-lastrow:yes'> <td width=118 valign=top style='width:88.8pt;border:solid white 1.0pt; mso-border-themecolor:background1;border-top:none;mso-border-top-alt:solid white .5pt; mso-border-top-themecolor:background1;mso-border-alt:solid white .5pt; mso-border-themecolor:background1;padding:0in 5.4pt 0in 5.4pt'> <p style='margin-right:4.5pt'><b style='mso-bidi-font-weight:normal'><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-no-proof:yes'><!--[if gte vml 1]><v:shape id="Picture_x0020_1" o:spid="_x0000_i1025" type="#_x0000_t75" alt="http://www.cs.cmu.edu/~htong/htong.jpg" style='width:1in;height:78.75pt;visibility:visible;mso-wrap-style:square'> <v:imagedata src="icde_tutorial_files/image003.jpg" o:title="htong"/> </v:shape><![endif]--><![if !vml]><img border=0 width=96 height=105 src="icde_tutorial_files/image004.jpg" alt="http://www.cs.cmu.edu/~htong/htong.jpg" v:shapes="Picture_x0020_1"><![endif]></span></b><b><span style='font-size:10.0pt;font-family:"Verdana","sans-serif"'><o:p></o:p></span></b></p> </td> <td width=697 valign=top style='width:522.8pt;border-top:none;border-left: none;border-bottom:solid white 1.0pt;mso-border-bottom-themecolor:background1; border-right:solid white 1.0pt;mso-border-right-themecolor:background1; mso-border-top-alt:solid white .5pt;mso-border-top-themecolor:background1; mso-border-left-alt:solid white .5pt;mso-border-left-themecolor:background1; mso-border-alt:solid white .5pt;mso-border-themecolor:background1;padding: 0in 5.4pt 0in 5.4pt'> <p><b><span style='font-size:10.0pt;font-family:"Verdana","sans-serif"'>Hanghang Tong</span></b><span style='font-size:10.0pt;font-family:"Verdana","sans-serif"'> is a senior Ph.D. student in the Machine Learning Department at Carnegie Mellon University. He has received best paper awards from&nbsp; SIAM-DM 2008 and ICDM 2006, and he has 25 refereed publications. He holds an M.S. degree and a B.S. degree from Tsinghua University, P.R. China. His research interests include data mining for multimedia and for graphs. (Full CV at&nbsp; <a href="http://www.cs.cmu.edu/~htong/pdf/cv_Tong.pdf" target="_blank" title="http://www.cs.cmu.edu/~htong/pdf/cv_Tong.pdf" id=xb56>www.cs.cmu.edu/~htong/pdf/cv_Tong.pdf</a> )<o:p></o:p></span></p> <p style='margin-right:4.5pt' id=w6-x0><b><span style='font-size:10.0pt; font-family:"Verdana","sans-serif"'><o:p>&nbsp;</o:p></span></b></p> </td> </tr> </table> <p><span style='font-size:10.0pt;font-family:"Verdana","sans-serif"'><br style='mso-special-character:line-break'> <![if !supportLineBreakNewLine]><br style='mso-special-character:line-break'> <![endif]><o:p></o:p></span></p> <p id=w6-x0><span style='font-size:10.0pt;font-family:"Verdana","sans-serif"'>&nbsp; <o:p></o:p></span></p> <p class=MsoNormal style='margin:0in;margin-bottom:.0001pt'><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: "Times New Roman"'><o:p>&nbsp;</o:p></span></p> </div> </body> </html>