Recent　years　have　witnessed　a　drastic　surge　in　graph　representation　learning,　which　usually　produces　low-dimensional　and　crisp　representations　from　graph　topology　and　high-dimensional　node　attributes.　Nevertheless,　a　crisp　representation　of　a　node　or　graph　actually　conceals　the　uncertainty　and　interpretability　of　features.　In　citation　networks,　for　example,　the　reference　between　two　papers　is　always　involved　with　fuzziness　denoting　the　correlation　degrees,　that　is,　one　connection　may　simultaneously　belong　to　strong　and　weak　references　in　different　beliefs.　The　uncertainty　in　node　connections　and　attributes　inspires　us　to　delve　into　fuzzy　representations.　This　paper,　for　the　first　time,　proposes　an　unsupervised　fuzzy　representation　learning　model　for　graphs　and　networks　to　improve　their　expressiveness　by　making　crisp　representations　fuzzy.　Specifically,　we　develop　a　fuzzy　graph　convolution　neural　network　(FGCNN),　which　could　aggregate　high-level　fuzzy　features,　leveraging　fuzzy　logic　to　fully　excavate　feature-level　uncertainties,　and　finally　generate　fuzzy　representations.　The　corresponding　hierarchical　model　composed　of　multiple　FGCNNs,　called　deep　fuzzy　graph　convolution　neural　network　(DFGCNN),　is　able　to　generate　fuzzy　node　representations　which　are　more　expressive　than　crisp　ones.　Experimental　results　of　multiple　network　analysis　tasks　validate　that　the　proposed　fuzzy　representation　models　have　strong　competitiveness　against　the　state-of-the-art　baselines　over　several　real-world　datasets.　IEEE