A network-based data mining approach to portfolio selection via weighted clique relaxations

Boginski, Vladimir; Butenko, Sergiy; Shirokikh, Oleg; Trukhanov, Svyatoslav; Lafuente, Jaime Gil

doi:10.1007/s10479-013-1395-3

Cited by 34 publications

(23 citation statements)

References 18 publications

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“…Identifying cohesive subgroups (not necessarily cliques and k-plexes) has also been performed in a number of non-biological networks: in studying terrorist and other criminal networks [14], web graphs [15], wireless networks [16], in finding structural patterns embedded within social network data [17], text mining [18], stock markets [19], etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Variable Neighborhood Search for Partitioning Sparse Biological Networks into the Maximum Edge-Weighted k-Plexes

Grbić

Kartelj

Janković

et al. 2020

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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In a network, a k-plex represents a subset of n vertices where the degree of each vertex in the subnetwork induced by this subset is at least n − k. The maximum edge-weight k-plex partitioning problem (Max-EkPP) is to find the k-plex partitioning in edge-weighted network, such that the sum of edge weights is maximal. The Max-EkPP has an important role in discovering new information in large sparse biological networks. We propose a variable neighborhood search (VNS) algorithm for solving Max-EkPP. The VNS implements a local search based on the 1-swap first improvement strategy and the objective function that takes into account the degree of every vertex in each partition. The objective function favors feasible solutions, also enabling a gradual increase in terms of objective function value when moving from slightly infeasible to barely feasible solutions.A comprehensive experimental computation is performed on real metabolic networks and other benchmark instances from literature. Comparing to the integer linear programming method from literature, our approach succeeds to find all known optimal solutions. For all other instances, the VNS either reaches previous best known solution or improves it. The proposed VNS is also tested on a large-scaled dataset which was not previously considered in literature.

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Section: Literature Reviewmentioning

confidence: 99%

Variable Neighborhood Search for Partitioning Sparse Biological Networks into the Maximum Edge-Weighted k-Plexes

Grbić

Kartelj

Janković

et al. 2020

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…Other authors used graph-theoretic concepts to explore the global properties of stock markets by analyzing the structure of the underlying graph. In [7] the authors solve different classical NP-hard optimization problems to analyze the dependencies among stocks. A maximum clique problem was solved for detecting large clusters of similar and dissimilar stocks according to the natural criterion of pairwise correlation.…”

Section: Introductionmentioning

confidence: 99%

Clustering and portfolio selection problems: A unified framework

Puerto

Rodríguez-Madrena

Scozzari

2020

Computers & Operations Research

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“…Despite mathematical elegance and physical intuition, direct vertex clustering is an NP hard problem. Consequently, existing graph-theoretic portfolio constructions employ combinatorial optimization formulations [12,19,20,21,22,23], which too become computationally intractable for large graph systems. To alleviate this issue, we employ the minimum cut vertex clustering method to introduce the portfolio cut.…”

Section: Introductionmentioning

confidence: 99%

Portfolio Cuts: A Graph-Theoretic Framework to Diversification

Dees

Stanković

Constantinides

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Investment returns naturally reside on irregular domains, however, standard multivariate portfolio optimization methods are agnostic to data structure. To this end, we investigate ways for domain knowledge to be conveniently incorporated into the analysis, by means of graphs. Next, to relax the assumption of the completeness of graph topology and to equip the graph model with practically relevant physical intuition, we introduce the portfolio cut paradigm. Such a graph-theoretic portfolio partitioning technique is shown to allow the investor to devise robust and tractable asset allocation schemes, by virtue of a rigorous graph framework for considering smaller, computationally feasible, and economically meaningful clusters of assets, based on graph cuts. In turn, this makes it possible to fully utilize the asset returns covariance matrix for constructing the portfolio, even without the requirement for its inversion. The advantages of the proposed framework over traditional methods are demonstrated through numerical simulations based on real-world price data.

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A network-based data mining approach to portfolio selection via weighted clique relaxations

Cited by 34 publications

References 18 publications

Variable Neighborhood Search for Partitioning Sparse Biological Networks into the Maximum Edge-Weighted k-Plexes

Variable Neighborhood Search for Partitioning Sparse Biological Networks into the Maximum Edge-Weighted k-Plexes

Clustering and portfolio selection problems: A unified framework

Portfolio Cuts: A Graph-Theoretic Framework to Diversification

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