A LinUCB contextual bandit selects federated aggregation rules online based on update variance, cosine similarity, and mean norm, claiming superior accuracy and tunable risk posture.
An A{\alpha}-spectral radius for the existence of {P3, P4, P5}-factors in graphs
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Let $G$ be a connected graph of order $n$ with $n\geq25$. A $\{P_3,P_4,P_5\}$-factor is a spanning subgraph $H$ of $G$ such that every component of $H$ is isomorphic to an element of $\{P_3,P_4,P_5\}$. Nikiforov introduced the $A_{\alpha}$-matrix of $G$ as $A_{\alpha}(G)=\alpha D(G)+(1-\alpha)A(G)$ [V. Nikiforov, Merging the $A$- and $Q$-spectral theories, Appl. Anal. Discrete Math. 11 (2017) 81--107], where $\alpha\in[0,1]$, $D(G)$ denotes the diagonal matrix of vertex degrees of $G$ and $A(G)$ denotes the adjacency matrix of $G$. The largest eigenvalue of $A_{\alpha}(G)$, denoted by $\lambda_{\alpha}(G)$, is called the $A_{\alpha}$-spectral radius of $G$. In this paper, it is proved that $G$ has a $\{P_3,P_4,P_5\}$-factor unless $G=K_1\vee(K_{n-2}\cup K_1)$ if $\lambda_{\alpha}(G)\geq\lambda_{\alpha}(K_1\vee(K_{n-2}\cup K_1))$, where $\alpha$ be a real number with $0\leq\alpha<\frac{2}{3}$.
fields
cs.LG 1years
2025 1verdicts
REJECT 1representative citing papers
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FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learning
A LinUCB contextual bandit selects federated aggregation rules online based on update variance, cosine similarity, and mean norm, claiming superior accuracy and tunable risk posture.