• Corpus ID: 254069376

On the complexity of quantum link prediction in complex networks

@inproceedings{Moutinho2022OnTC,
  title={On the complexity of quantum link prediction in complex networks},
  author={Jo{\~a}o P. Moutinho and D. Magano and Bruno Coelho Coutinho},
  year={2022}
}
Link prediction methods use patterns in known network data to infer which connections may be missing. Previous work has shown that continuous-time quantum walks can be used to represent path-based link prediction, which we further study here to develop a more optimized quantum algorithm. Using a sampling framework for link prediction, we analyze the query access to the input network required to produce a certain number of prediction samples. Considering both well-known classical path-based… 
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References

SHOWING 1-10 OF 59 REFERENCES

Quantum Link Prediction in Complex Networks

A quantum algorithm for path-based link prediction, QLP, is proposed using a controlled continuous-time quantum walk to encode even and odd path- based prediction scores, and it is confirmed that the quantum walk scoring function performs similarly to other path-Based link predictors.

Link prediction with continuous-time classical and quantum walks

The results show that continuous-time classical random walks and quantum walks using the network adjacency matrix can successfully predict missing protein-protein interactions, with performance rivalling the state of the art.

Adaptive Network Automata Modelling of Complex Networks

This study compares Cannistraci-Hebb adaptive (CHA) network automaton against state-of-the-art link prediction methods such as structural perturbation method (SPM), stochastic block models (SBM) and artificial intelligence algorithms for graph embedding and highlights that CHA offers the key advantage to explicitly explain the mechanistic rule of self-organization which leads to the link prediction performance, whereas SPM and graph embeding not.

Link prediction via linear optimization

Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks

The first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain are introduced, and a local-based formalism is presented that allows to intuitively implement neighbourhood-based link prediction entirely in the bipartsite domain.

Quadratic speedup for spatial search by continuous-time quantum walk

This article provides a new continuous-time quantum walk search algorithm that can find a marked node in any graph with any number of marked nodes, in a time that is quadratically faster than classical random walks.

Optimal Hamiltonian Simulation by Quantum Signal Processing.

It is argued that physical intuition can lead to optimal simulation methods by showing that a focus on simple single-qubit rotations elegantly furnishes an optimal algorithm for Hamiltonian simulation, a universal problem that encapsulates all the power of quantum computation.

Spatial search by quantum walk

This work considers an alternative search algorithm based on a continuous-time quantum walk on a graph and shows that full {radical}(N) speedup can be achieved on a d-dimensional periodic lattice for d>4.

Link Prediction in Complex Networks: A Survey

Symmetries, Graph Properties, and Quantum Speedups

It is proved that hypergraph symmetries in the adjacency matrix model allow at most a polynomial separation between randomized and quantum query complexities, and it is shown that permutation groups constructed out of these symmetry are essentially the only permutations groups that prevent super-polynomial quantum speedups.
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