Enrichment and aggregation of topological motifs are independent organizational principles of integrated interaction networks

Topological network motifs represent functional relationships within and between regulatory and protein-protein interaction networks. Enriched motifs often aggregate into self-contained units forming functional modules. Theoretical models for network evolution by duplication-divergence mechanisms and for network topology by hierarchical scale-free networks have suggested a one-to-one relation between network motif enrichment and aggregation, but this relation has never been tested quantitatively in real biological interaction networks. Here we introduce a novel method for assessing the statistical significance of network motif aggregation and for identifying clusters of overlapping network motifs. Using an integrated network of transcriptional, posttranslational and protein-protein interactions in yeast we show that network motif aggregation reflects a local modularity property which is independent of network motif enrichment. In particular our method identified novel functional network themes for a set of motifs which are not enriched yet aggregate significantly and challenges the conventional view that network motif enrichment is the most basic organizational principle of complex networks.Comment: 12 pages, 5 figure

Principled Multilayer Network Embedding

Multilayer network analysis has become a vital tool for understanding different relationships and their interactions in a complex system, where each layer in a multilayer network depicts the topological structure of a group of nodes corresponding to a particular relationship. The interactions among different layers imply how the interplay of different relations on the topology of each layer. For a single-layer network, network embedding methods have been proposed to project the nodes in a network into a continuous vector space with a relatively small number of dimensions, where the space embeds the social representations among nodes. These algorithms have been proved to have a better performance on a variety of regular graph analysis tasks, such as link prediction, or multi-label classification. In this paper, by extending a standard graph mining into multilayer network, we have proposed three methods ("network aggregation," "results aggregation" and "layer co-analysis") to project a multilayer network into a continuous vector space. From the evaluation, we have proved that comparing with regular link prediction methods, "layer co-analysis" achieved the best performance on most of the datasets, while "network aggregation" and "results aggregation" also have better performance than regular link prediction methods

On the Marginal Cost of Road Congestion: an Evaluation Method with Application to the Paris Region

The paper analyzes the sensitivity of the marginal congestion cost on a roadway network to the level of aggregation in space, from utmost disaggregate to utmost aggregate. Simulation and aggregation are based on a static network assignment model.Marginal cost ; Road congestion ; Cost aggregation ; Congestion indicator ; Assignment model

On the Potential of Generic Modeling for VANET Data Aggregation Protocols

In-network data aggregation is a promising communication mechanism to reduce bandwidth requirements of applications in vehicular ad-hoc networks (VANETs). Many aggregation schemes have been proposed, often with varying features. Most aggregation schemes are tailored to specific application scenarios and for specific aggregation operations. Comparative evaluation of different aggregation schemes is therefore difficult. An application centric view of aggregation does also not tap into the potential of cross application aggregation. Generic modeling may help to unlock this potential. We outline a generic modeling approach to enable improved comparability of aggregation schemes and facilitate joint optimization for different applications of aggregation schemes for VANETs. This work outlines the requirements and general concept of a generic modeling approach and identifies open challenges