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Fülöp Bazsó

Publications and source records attributed to Fülöp Bazsó.

4 recordsLinked to original sources

Zero-divisor algebras of graph functions: quantum caging, entangling routing and stochastic first-passage exclusion

We construct Lie-bialgebraic differential structures on vertex functions of a finite graph using coefficients in commutative algebras with zero divisors. We derive the exact Jacobi criterion for the graph bracket and classify its solutions. Over an integral domain, each connected component of the nonzero support is a uniformly weighted clique; over $\mathbb C^q$, the general solution is a superposition of such clique layers. A four-vertex diamond built from overlapping triangle layers shows that Jacobi compatibility is strictly broader than the matching geometry generated by proper edge colouring. For the canonical cobracket, we prove a rigidity theorem over commutative $2$-torsion-free rings: Lie-bialgebra compatibility is equivalent to the local annihilation condition $w_{ij}w_{ik}=0$ for distinct incident edges. Hence the canonical bialgebra selects matching layers from the wider Jacobi-compatible class. The same structure makes the weighted graph Laplacian an inner derivation and yields an incidence-type vertex--edge calculus with a positive squared-Laplacian factorization. Representing the idempotent channels by internal-state projectors gives exact quantum caging and channel-controlled transfer that creates path--channel entanglement. The ordered real realization gives an intrinsic graph Fokker--Planck equation, exact first-passage exclusion, and a solvable crossover to escape under weak channel switching. These models are deliberately reducible; their role is to exhibit the quantum and stochastic consequences of the matching geometry selected by canonical bialgebra compatibility.

math-ph↗

Regular Decomposition: an information and graph theoretic approach to stochastic block models

A method for compression of large graphs and non-negative matrices to a block structure is proposed. Szemerédi's regularity lemma is used as heuristic motivation of the significance of stochastic block models. Another ingredient of the method is Rissanen's minimum description length principle (MDL). We propose practical algorithms and provide theoretical results on the accuracy of the method.

cs.IT↗

Regular decomposition of large graphs and other structures: scalability and robustness towards missing data

A method for compression of large graphs and matrices to a block structure is further developed. Szemerédi's regularity lemma is used as a generic motivation of the significance of stochastic block models. Another ingredient of the method is Rissanen's minimum description length principle (MDL). We continue our previous work on the subject, considering cases of missing data and scaling of algorithms to extremely large size of graphs. In this way it would be possible to find out a large scale structure of a huge graphs of certain type using only a tiny part of graph information and obtaining a compact representation of such graphs useful in computations and visualization.

cs.IT↗

Fuzzy communities and the concept of bridgeness in complex networks

We consider the problem of fuzzy community detection in networks, which complements and expands the concept of overlapping community structure. Our approach allows each vertex of the graph to belong to multiple communities at the same time, determined by exact numerical membership degrees, even in the presence of uncertainty in the data being analyzed. We created an algorithm for determining the optimal membership degrees with respect to a given goal function. Based on the membership degrees, we introduce a new measure that is able to identify outlier vertices that do not belong to any of the communities, bridge vertices that belong significantly to more than one single community, and regular vertices that fundamentally restrict their interactions within their own community, while also being able to quantify the centrality of a vertex with respect to its dominant community. The method can also be used for prediction in case of uncertainty in the dataset analyzed. The number of communities can be given in advance, or determined by the algorithm itself using a fuzzified variant of the modularity function. The technique is able to discover the fuzzy community structure of different real world networks including, but not limited to social networks, scientific collaboration networks and cortical networks with high confidence.

physics.soc-ph↗