IEEE Trans Vis Comput Graph - A Modular Degree-of-Interest Specification for the Visual Analysis of Large Dynamic Networks.

Tópicos

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

Resumo

Large dynamic networks are targets of analysis in many fields. Tracking temporal changes at scale in these networks is challenging due in part to the fact that small changes can be missed or drowned-out by the rest of the network. For static networks, current approaches allow the identification of specific network elements within their context. However, in the case of dynamic networks, the user is left alone with finding salient local network elements and tracking them over time. In this work, we introduce a modular DoI specification to flexibly define what salient changes are and to assign them a measure of their importance in a time-varying setting. The specification takes into account neighborhood structure information, numerical attributes of nodes/edges, and their temporal evolution. A tailored visualization of the DoI specification complements our approach. Alongside a traditional node-link view of the dynamic network, it serves as an interface for the interactive definition of a DoI function. By using it to successively refine and investigate the captured details, it supports the analysis of dynamic networks from an initial view until pinpointing a user's analysis goal. We report on applying our approach to scientific co-authorship networks and give concrete results for the DBLP dataset.

Resumo Limpo

larg dynam network target analysi mani field track tempor chang scale network challeng due part fact small chang can miss drownedout rest network static network current approach allow identif specif network element within context howev case dynam network user left alon find salient local network element track time work introduc modular doi specif flexibl defin salient chang assign measur import timevari set specif take account neighborhood structur inform numer attribut nodesedg tempor evolut tailor visual doi specif complement approach alongsid tradit nodelink view dynam network serv interfac interact definit doi function use success refin investig captur detail support analysi dynam network initi view pinpoint user analysi goal report appli approach scientif coauthorship network give concret result dblp dataset

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