Topological similarity as a proxy to content similarity

Rosen, Yonatan; Louzoun, Yoram

doi:10.1093/comnet/cnv012

Cited by 5 publications

(6 citation statements)

References 62 publications

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“…Furthermore, we also determined that the average shortest path in the modules (i.e., 2.25 in IBGLL) was shorter than that in the pathways (i.e., 3.82 in PID), because topological modules contain only proteins exhibiting dense interaction. Thus, a combination of other valuable biological and topological information may facilitate the effective clustering of non-adjacent proteins 40 into one module as a new pathway.…”

Section: Discussionmentioning

confidence: 99%

Functional diversity of topological modules in human protein-protein interaction networks

Wang

Chu

et al. 2017

Sci Rep

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A large-scale molecular interaction network of protein-protein interactions (PPIs) enables the automatic detection of molecular functional modules through a computational approach. However, the functional modules that are typically detected by topological community detection algorithms may be diverse in functional homogeneity and are empirically considered to be default functional modules. Thus, a significant challenge that has been described but not elucidated is investigating the relationship between topological modules and functional modules. We systematically investigated this issue by initially using seven widely used community detection algorithms to partition the PPI network into communities. Four homogeneity measures were subsequently implemented to evaluate the functional homogeneity of protein community. We determined that a significant portion of topological modules with heterogeneous functionality exists and should be further investigated; moreover, these findings indicated that topologically based functional module detection approaches must be reconsidered. Furthermore, we found that the functional homogeneity of topological modules is positively correlated with their edge densities, degree of association with diseases and general Gene Ontology (GO) terms. Thus, topologically based module detection approaches should be used with caution in the identification of functional modules with high homogeneity

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Section: Discussionmentioning

confidence: 99%

Functional diversity of topological modules in human protein-protein interaction networks

Wang

Chu

et al. 2017

Sci Rep

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“…using a large set of topological measures and a Random Forest classifier. The combined features approach [25] significantly outperformed the K-Core based classification ( Fig. 4).…”

Section: The Average Clustering Coefficient Ismentioning

confidence: 97%

A combined network and machine learning approaches for product market forecasting

Fan,

Cohen,

Shekhtman

et al. 2018

Preprint

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Sustainable financial markets play an important role in the functioning of human society. Still, the detection and prediction of risk in financial markets remain challenging and draw much attention from the scientific community. Here we develop a new approach based on combined network theory and machine learning to study the structure and operations of financial product markets. Our network links are based on the similarity of firms' products and are constructed using the Securities Exchange Commission (SEC) filings of US listed firms. We find that several features in our network can serve as good precursors of financial market risks. We then combine the network topology and machine learning methods to predict both successful and failed firms. We find that the forecasts made using our method are much better than other well-known regression techniques. The framework presented here not only facilitates the prediction of financial markets but also provides insight and demonstrate the power of combining network theory and machine learning.

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“…Different colors represent different shells (Carmi et al 2007) Network topology measures for machine learning Multiple network topological measures have been used to predict the future gain of companies, as well as the probability of their collapse. The general approach is based on methods described in Rosen et al (2016) and Naaman et al (2018). In brief, a vector representing a set of topological features has been computed for each node, and this vector was then used in a machine learning framework as decribed below.…”

Section: Product Similarity Networkmentioning

confidence: 99%

“…However, recently a complementary approach suggested that, in contrast with images that are typically overlaid on a 2D lattice, graphs have a complex topology. This topology is highly informative of the properties of nodes and edges (Rosen and Louzoun 2015;Naaman et al 2018;Benami et al 2019) and can thus be used to classify their classes. We here propose that such topology-based methods can be used to predict companies future performance.…”

Section: Introductionmentioning

confidence: 99%

Topology of products similarity network for market forecasting

et al. 2019

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The detection and prediction of risk in financial markets is one of the main challenges of economic forecasting, and draws much attention from the scientific community. An even more challenging task is the prediction of the future relative gain of companies. We here develop a novel combination of product text analysis, network theory and topological based machine learning to study the future performance of companies in financial markets. Our network links are based on the similarity of firms' products and constructed using the Securities Exchange Commission (SEC) filings of US listed firms. We find that several topological features of this network can serve as good precursors of risks or future gain of companies. We then apply machine learning to network attributes vectors for each node to predict successful and failing firms. The resulting accuracies are much better than current state of the art techniques. The framework presented here not only facilitates the prediction of financial markets but also provides insight and demonstrates the power of combining network theory and topology based machine learning.

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Topological similarity as a proxy to content similarity

Cited by 5 publications

References 62 publications

Functional diversity of topological modules in human protein-protein interaction networks

Functional diversity of topological modules in human protein-protein interaction networks

A combined network and machine learning approaches for product market forecasting

Topology of products similarity network for market forecasting

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