IEEE Trans Vis Comput Graph - Visual Analysis of Large Graphs Using (X,Y)-clustering and Hybrid Visualizations.

Tópicos

{ structur(1116) can(940) graph(676) }
{ method(1969) cluster(1462) data(1082) }
{ concept(1167) ontolog(924) domain(897) }
{ method(1557) propos(1049) approach(1037) }
{ imag(1947) propos(1133) code(1026) }
{ method(2212) result(1239) propos(1039) }
{ inform(2794) health(2639) internet(1427) }
{ imag(2830) propos(1344) filter(1198) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ learn(2355) train(1041) set(1003) }
{ data(1714) softwar(1251) tool(1186) }
{ case(1353) use(1143) diagnosi(1136) }
{ data(2317) use(1299) case(1017) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ health(1844) social(1437) communiti(874) }
{ implement(1333) system(1263) develop(1122) }
{ take(945) account(800) differ(722) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ algorithm(1844) comput(1787) effici(935) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ studi(1410) differ(1259) use(1210) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ first(2504) two(1366) second(1323) }
{ use(976) code(926) identifi(902) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ clinic(1479) use(1117) guidelin(835) }
{ extract(1171) text(1153) clinic(932) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Many different approaches have been proposed for the challenging problem of visually analyzing large networks. Clustering is one of the most promising. In this paper we propose a new clustering technique whose goal is that of producing both intra-cluster graphs and inter-cluster graph with desired topological properties. We formalize this concept in the (X,Y)-clustering framework, where Y is the class that defines the desired topological properties of intracluster graphs and X is the class that defines the desired topological properties of the inter-cluster graph. By exploiting this approach hybrid visualization tools can effectively combine different node-link and matrix-based representations, allowing users to interactively explore the graph by expansion/contraction of clusters without loosing their mental map. As a proof of concept, we describe the system VHYXY (Visual Hybrid (X,Y)-clustering) that implements our approach and we present the results of case studies to the visual analysis of social networks.

Resumo Limpo

mani differ approach propos challeng problem visual analyz larg network cluster one promis paper propos new cluster techniqu whose goal produc intraclust graph interclust graph desir topolog properti formal concept xyclust framework y class defin desir topolog properti intraclust graph x class defin desir topolog properti interclust graph exploit approach hybrid visual tool can effect combin differ nodelink matrixbas represent allow user interact explor graph expansioncontract cluster without loos mental map proof concept describ system vhyxi visual hybrid xyclust implement approach present result case studi visual analysi social network

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