IEEE Trans Vis Comput Graph - Dynamic Network Visualization with Extended Massive Sequence Views.

Tópicos

{ structur(1116) can(940) graph(676) }
{ network(2748) neural(1063) input(814) }
{ sequenc(1873) structur(1644) protein(1328) }
{ data(1737) use(1416) pattern(1282) }
{ data(3963) clinic(1234) research(1004) }
{ can(774) often(719) complex(702) }
{ chang(1828) time(1643) increas(1301) }
{ research(1085) discuss(1038) issu(1018) }
{ visual(1396) interact(850) tool(830) }
{ studi(1119) effect(1106) posit(819) }
{ model(3404) distribut(989) bayesian(671) }
{ inform(2794) health(2639) internet(1427) }
{ imag(1057) registr(996) error(939) }
{ featur(3375) classif(2383) classifi(1994) }
{ take(945) account(800) differ(722) }
{ framework(1458) process(801) describ(734) }
{ risk(3053) factor(974) diseas(938) }
{ data(2317) use(1299) case(1017) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ activ(1452) weight(1219) physic(1104) }
{ data(1714) softwar(1251) tool(1186) }
{ howev(809) still(633) remain(590) }
{ import(1318) role(1303) understand(862) }
{ state(1844) use(1261) util(961) }
{ cost(1906) reduc(1198) effect(832) }
{ use(2086) technolog(871) perceiv(783) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ method(2212) result(1239) propos(1039) }
{ imag(1947) propos(1133) code(1026) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ bind(1733) structur(1185) ligand(1036) }
{ method(1219) similar(1157) match(930) }
{ imag(2830) propos(1344) filter(1198) }
{ 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) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ 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) }
{ case(1353) use(1143) diagnosi(1136) }
{ studi(1410) differ(1259) use(1210) }
{ perform(999) metric(946) measur(919) }
{ system(1050) medic(1026) inform(1018) }
{ model(2341) predict(2261) use(1141) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ 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) }
{ 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) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ analysi(2126) use(1163) compon(1037) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Networks are present in many fields such as finance, sociology, and transportation. Often these networks are dynamic: they have a structural as well as a temporal aspect. In addition to relations occurring over time, node information is frequently present such as hierarchical structure or time-series data. We present a technique that extends the Massive Sequence View (MSV) for the analysis of temporal and structural aspects of dynamic networks. Using features in the data as well as Gestalt principles in the visualization such as closure, proximity, and similarity, we developed node reordering strategies for the MSV to make these features stand out that optionally take the hierarchical node structure into account. This enables users to find temporal properties such as trends, counter trends, periodicity, temporal shifts, and anomalies in the network as well as structural properties such as communities and stars. We introduce the circular MSV that further reduces visual clutter. In addition, the (circular) MSV is extended to also convey time-series data associated with the nodes. This enables users to analyze complex correlations between edge occurrence and node attribute changes. We show the effectiveness of the reordering methods on both synthetic and a rich real-world dynamic network data set.

Resumo Limpo

network present mani field financ sociolog transport often network dynam structur well tempor aspect addit relat occur time node inform frequent present hierarch structur timeseri data present techniqu extend massiv sequenc view msv analysi tempor structur aspect dynam network use featur data well gestalt principl visual closur proxim similar develop node reorder strategi msv make featur stand option take hierarch node structur account enabl user find tempor properti trend counter trend period tempor shift anomali network well structur properti communiti star introduc circular msv reduc visual clutter addit circular msv extend also convey timeseri data associ node enabl user analyz complex correl edg occurr node attribut chang show effect reorder method synthet rich realworld dynam network data set

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