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Alignment of dynamic networks
Social and Information Networks · Submitted 30 Jan 2017
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We confirm our hypothesis that dynamic NA is superior to static NA, under fair comparison conditions, on synthetic and real-world networks, in computational biology and social network domains.
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The abstract, sentence by sentence
Networks can model real-world systems in a variety of domains. Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems' static network representations, as is currently done. For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized using any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, under fair comparison conditions, on synthetic and real-world networks, in computational biology and social network domains. DynaMAGNA++ is parallelized and it includes a user-friendly graphical interface.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Networks can model real-world systems in a variety of domains. | background 1.00 | 0.03 | 0.01 | asserted 0.86 |
| s1 | Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. | background 1.00 | 0.05 | 0.02 | asserted 0.69 |
| s3 | Existing NA methods can only align static networks. | gap 0.97 | 0.04 | 0.02 | asserted 1.00 |
| s4 | However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. | background 0.76 | 0.06 | 0.02 | asserted 0.78 |
| s5 | We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems' static network representations, as is currently done. | gap 0.37 | 0.12 | 0.13 | speculative 0.67 |
| s6 | For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA++. | contribution 1.00 | 0.93 | 0.04 | asserted 1.00 |
| s7 | This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. | method 0.73 | 0.76 | 0.02 | asserted 1.00 |
| s8 | Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. | method 0.93 | 0.70 | 0.05 | asserted 1.00 |
| s9 | For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. | contribution 0.63 | 0.91 | 0.03 | asserted 1.00 |
| s10 | Importantly, the two dynamic conservation measures can be optimized using any state-of-the-art NA method and not just MAGNA++. | contribution 0.75 | 0.51 | 0.04 | asserted 0.99 |
| s11 | We confirm our hypothesis that dynamic NA is superior to static NA, under fair comparison conditions, on synthetic and real-world networks, in computational biology and social network domains. | result 0.99 | 0.96 | 0.93 | asserted 1.00 |
| s12 | DynaMAGNA++ is parallelized and it includes a user-friendly graphical interface. | contribution 0.65 | 0.85 | 0.02 | asserted 1.00 |
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Original abstract
Networks can model real-world systems in a variety of domains. Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems' static network representations, as is currently done. For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized using any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, under fair comparison conditions, on synthetic and real-world networks, in computational biology and social network domains. DynaMAGNA++ is parallelized and it includes a user-friendly graphical interface.
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