Recent advances in sequencing allow population-genomic data to be generated for virtually any species. However, approaches to analyze such data lag behind the ability to generate it, particularly in non-model species. Linkage disequilibrium (LD, the non-random association of alleles from different loci) is a highly sensitive indicator of many evolutionary phenomena including chromosomal inversions, local adaptation and geographical structure. Here we present linkage disequilibrium network analysis (LDna), which accesses information on LD shared between multiple loci genome-wide. In LD networks, vertices represent loci and connections between vertices represent the LD between them. We analyzed such networks in two test cases: a new Restriction-site Associated DNA sequence (RAD-seq) dataset for Anopheles baimaii, a Southeast Asian malaria vector; and a well-characterised single nucleotide polymorphism (SNP) dataset from 21 three-spined stickleback individuals. In each case we readily identified five distinct LD network clusters (single outlier clusters, SOCs), each comprising many loci connected by high LD. In A. baimaii, further population-genetic analyses supported the inference that each SOC corresponds to a large inversion, consistent with previous cytological studies. For sticklebacks, we inferred that each SOC was associated with a distinct evolutionary phenomenon: two chromosomal inversions, local adaptation, population-demographic history and geographic structure. LDna is thus a useful exploratory tool, able to give a global overview of LD associated with diverse evolutionary phenomena and identify loci potentially involved. LDna does not require a linkage map or reference genome, so is applicable to any population-genomic dataset, making it especially valuable for non-model species. This article is protected by copyright. All rights reserved.