Dmitry Gubanov

Aleksei Pupyshev, Dmitry Gubanov, Elshan Dzhafarov et al.

This paper intends to propose the architecture of a blockchain-agnostic protocol designed for communication of blockchains amongst each other (i.e. cross-chain), and for blockchains with the outside world (i.e. data oracles). The expansive growth of cutting-edge technology in the blockchain industry outlines the need and opportunity for addressing oracle consensus in a manner both technologically and economically efficient as well as futureproof. Blockchain-agnosticism is inherently limited if proposing a technological solution involves adding one more architectural layer. As such, Gravity protocol is designed to be a truly blockchain-agnostic protocol. By ensuring parity through direct integration and by leveraging the stability and security of the respective interconnected ecosystems, Gravity circumvents the need for a dedicated, public blockchain and a native token. Ultimately, Gravity protocol intends to address scalability challenges by providing a solid infrastructure for the creation of gateways, cross-chain applications, and sidechains. This paper introduces and defines the concept of Oracle Consensus and its implementation in the Gravity protocol named the Pulse Consensus algorithm. The proposed consensus architecture allows Gravity to be considered a singular decentralized blockchain-agnostic oracle.

Pavel Chebotarev, Dmitry Gubanov

Centrality metrics play a crucial role in network analysis, while the choice of specific measures significantly influences the accuracy of conclusions as each measure represents a unique concept of node importance. Among over 400 proposed indices, selecting the most suitable ones for specific applications remains a challenge. Existing approaches -- model-based, data-driven, and axiomatic -- have limitations, requiring association with models, training datasets, or restrictive axioms for each specific application. To address this, we introduce the culling method, which relies on the expert concept of centrality behavior on simple graphs. The culling method involves forming a set of candidate measures, generating a list of as small graphs as possible needed to distinguish the measures from each other, constructing a decision-tree survey, and identifying the measure consistent with the expert's concept. We apply this approach to a diverse set of 40 centralities, including novel kernel-based indices, and combine it with the axiomatic approach. Remarkably, only 13 small 1-trees are sufficient to separate all 40 measures, even for pairs of closely related ones. By adopting simple ordinal axioms like Self-consistency or Bridge axiom, the set of measures can be drastically reduced making the culling survey short. Applying the culling method provides insightful findings on some centrality indices, such as PageRank, Bridging, and dissimilarity-based Eigencentrality measures, among others. The proposed approach offers a cost-effective solution in terms of labor and time, complementing existing methods for measure selection, and providing deeper insights into the underlying mechanisms of centrality measures.