Matches in SemOpenAlex for { <https://semopenalex.org/work/W3048258051> ?p ?o ?g. }
Showing items 1 to 84 of
84
with 100 items per page.
- W3048258051 abstract "In temporal ordered clustering, given a single snapshot of a dynamic network in which nodes arrive at distinct time instants, we aim at partitioning its nodes into $K$ ordered clusters $mathcal{C}_1 prec cdots prec mathcal{C}_K$ such that for $i<j$, nodes in cluster $mathcal{C}_i$ arrived before nodes in cluster $mathcal{C}_j$, with $K$ being a data-driven parameter and not known upfront. Such a problem is of considerable significance in many applications ranging from tracking the expansion of fake news to mapping the spread of information. We first formulate our problem for a general dynamic graph, and propose an integer programming framework that finds the optimal clustering, represented as a strict partial order set, achieving the best precision (i.e., fraction of successfully ordered node pairs) for a fixed density (i.e., fraction of comparable node pairs). We then develop a sequential importance procedure and design unsupervised and semi-supervised algorithms to find temporal ordered clusters that efficiently approximate the optimal solution. To illustrate the techniques, we apply our methods to the vertex copying (duplication-divergence) model which exhibits some edge-case challenges in inferring the clusters as compared to other network models. Finally, we validate the performance of the proposed algorithms on synthetic and real-world networks." @default.
- W3048258051 created "2020-08-13" @default.
- W3048258051 creator A5000657571 @default.
- W3048258051 creator A5011996079 @default.
- W3048258051 creator A5019536160 @default.
- W3048258051 date "2019-05-02" @default.
- W3048258051 modified "2023-09-27" @default.
- W3048258051 title "Temporal Ordered Clustering in Dynamic Networks: Unsupervised and Semi-supervised Learning Algorithms" @default.
- W3048258051 cites W1778020744 @default.
- W3048258051 cites W1908543963 @default.
- W3048258051 cites W2012853908 @default.
- W3048258051 cites W2074759400 @default.
- W3048258051 cites W2102524069 @default.
- W3048258051 cites W2132120234 @default.
- W3048258051 cites W2145977038 @default.
- W3048258051 cites W2169526870 @default.
- W3048258051 cites W2610360795 @default.
- W3048258051 cites W2755088640 @default.
- W3048258051 cites W2783382315 @default.
- W3048258051 cites W2913230220 @default.
- W3048258051 cites W2922346813 @default.
- W3048258051 cites W2937048519 @default.
- W3048258051 cites W2963096160 @default.
- W3048258051 cites W3080251964 @default.
- W3048258051 cites W3096001313 @default.
- W3048258051 hasPublicationYear "2019" @default.
- W3048258051 type Work @default.
- W3048258051 sameAs 3048258051 @default.
- W3048258051 citedByCount "0" @default.
- W3048258051 crossrefType "posted-content" @default.
- W3048258051 hasAuthorship W3048258051A5000657571 @default.
- W3048258051 hasAuthorship W3048258051A5011996079 @default.
- W3048258051 hasAuthorship W3048258051A5019536160 @default.
- W3048258051 hasConcept C111919701 @default.
- W3048258051 hasConcept C11413529 @default.
- W3048258051 hasConcept C114614502 @default.
- W3048258051 hasConcept C154945302 @default.
- W3048258051 hasConcept C164866538 @default.
- W3048258051 hasConcept C199360897 @default.
- W3048258051 hasConcept C33923547 @default.
- W3048258051 hasConcept C41008148 @default.
- W3048258051 hasConcept C55282118 @default.
- W3048258051 hasConcept C73555534 @default.
- W3048258051 hasConcept C8038995 @default.
- W3048258051 hasConcept C80444323 @default.
- W3048258051 hasConceptScore W3048258051C111919701 @default.
- W3048258051 hasConceptScore W3048258051C11413529 @default.
- W3048258051 hasConceptScore W3048258051C114614502 @default.
- W3048258051 hasConceptScore W3048258051C154945302 @default.
- W3048258051 hasConceptScore W3048258051C164866538 @default.
- W3048258051 hasConceptScore W3048258051C199360897 @default.
- W3048258051 hasConceptScore W3048258051C33923547 @default.
- W3048258051 hasConceptScore W3048258051C41008148 @default.
- W3048258051 hasConceptScore W3048258051C55282118 @default.
- W3048258051 hasConceptScore W3048258051C73555534 @default.
- W3048258051 hasConceptScore W3048258051C8038995 @default.
- W3048258051 hasConceptScore W3048258051C80444323 @default.
- W3048258051 hasLocation W30482580511 @default.
- W3048258051 hasOpenAccess W3048258051 @default.
- W3048258051 hasPrimaryLocation W30482580511 @default.
- W3048258051 hasRelatedWork W1987600084 @default.
- W3048258051 hasRelatedWork W1997887516 @default.
- W3048258051 hasRelatedWork W2112809960 @default.
- W3048258051 hasRelatedWork W2135827982 @default.
- W3048258051 hasRelatedWork W2150560146 @default.
- W3048258051 hasRelatedWork W2889658189 @default.
- W3048258051 hasRelatedWork W2911597709 @default.
- W3048258051 hasRelatedWork W2918668824 @default.
- W3048258051 hasRelatedWork W2943301837 @default.
- W3048258051 hasRelatedWork W2951553909 @default.
- W3048258051 hasRelatedWork W2954398424 @default.
- W3048258051 hasRelatedWork W2963090360 @default.
- W3048258051 hasRelatedWork W2967803212 @default.
- W3048258051 hasRelatedWork W2968108372 @default.
- W3048258051 hasRelatedWork W2993070183 @default.
- W3048258051 hasRelatedWork W2994589319 @default.
- W3048258051 hasRelatedWork W3010858612 @default.
- W3048258051 hasRelatedWork W3099453919 @default.
- W3048258051 hasRelatedWork W3112278336 @default.
- W3048258051 hasRelatedWork W3206808214 @default.
- W3048258051 isParatext "false" @default.
- W3048258051 isRetracted "false" @default.
- W3048258051 magId "3048258051" @default.
- W3048258051 workType "article" @default.