Clustering　is　an　unsupervised　learning　task　that　models　data　as　coherent　groups.　Multiple　approaches　have　been　proposed　in　the　past　to　cluster　large　volumes　of　data.　Graphs　provide　a　logical　mapping　of　many　real-world　datasets　rich　enough　to　reflect　various　peculiarities　of　numerous　domains.　Apart　from　k-means,　k-medoid,　and　other　well-known　clustering　algorithms,　utilization　of　random　walk-based　approaches　to　cluster　data　is　a　prominent　area　of　data　mining　research.　Markov　clustering　algorithm　and　limited　random　walk-based　clustering　are　the　prominent　techniques　that　utilize　the　concept　of　random　walk.　The　main　goal　of　this　work　is　to　address　the　task　of　clustering　graphs　using　an　efficient　random　walk-based　method.　A　novel　walk　approach　in　a　graph　is　presented　here　that　determines　the　weight　of　the　edges　and　the　degree　of　the　nodes.　This　information　is　utilized　by　the　pseudo-guidance　model　to　guide　the　random　walk　procedure.　This　work　introduces　the　friends-of-friends　concept　during　the　random　walk　process　so　that　the　edges?　weights　are　determined　utilizing　an　inclusive　criterion.　This　concept　enables　a　random　walk　to　be　initiated　from　the　highest　degree　node.　The　random　walk　continues　until　the　walking　agent　cannot　find　any　unvisited　neighbor(s).　The　agent　walks　to　its　neighbors　if　it　finds　a　weight　of　one　or　more,　otherwise　the　agent?s　stopping　criteria　is　met.　The　nodes　visited　in　this　walk　form　a　cluster.　Once　a　walk　comes　to　halt,　the　visited　nodes　are　removed　from　the　original　graph　and　the　next　walk　starts　in　the　remaining　graph.　This　process　continues　until　all　nodes　of　the　graph　are　traversed.　The　focus　of　this　work　remains　random　walk-based　clustering　of　graphs.　The　proposed　approach　is　evaluated　using　18　real-world　benchmark　datasets　utilizing　six　cluster　validity　indices,　namely　Davies-Bouldin　index　(DBI),　Dunn　index　(DI),　Silhouette　coefficient　(SC),　Calinski-Harabasz　index　(CHI),　modularity　index,　and　normalized　cut.　This　proposal　is　compared　with　seven　closely　related　approaches　from　the　same　domain,　namely,　limited　random　walk,　pairwise　clustering,　personalized　page　rank　clustering,　GAKH　(genetic　algorithm　krill　herd)　graph　clustering,　mixing　time　of　random　walks,　density-based　clustering　of　large　probabilistic　graphs,　and　Walktrap.　Experiments　suggest　better　performance　of　this　work　based　on　the　evaluation　metrics.