Most　existing　weakly　supervised　crowd　counting　methods　utilize　Convolutional　Neural　Networks　(CNN)　or　Transformer　to　estimate　the　total　number　of　individuals　in　an　image.　However,　both　CNN-based　(grid-to-count　paradigm)　and　Transformer-based　(sequence-to-count　paradigm)　methods　take　images　as　inputs　in　a　regular　form.　This　approach　treats　all　pixels　equally　but　cannot　address　the　uneven　distribution　problem　within　human　crowds.　This　challenge　would　lead　to　a　decline　in　the　counting　performance　of　the　model.　Compared　with　grid　and　sequence,　the　graph　structure　could　better　explore　the　relationship　among　features.　In　this　article,　we　propose　a　new　graph-based　crowd　counting　method　named　CrowdGraph,　which　reinterprets　the　weakly　supervised　crowd　counting　problem　from　a　graph-to-count　perspective.　In　the　proposed　CrowdGraph,　each　image　is　constructed　as　a　graph,　and　a　graph-based　network　is　designed　to　extract　features　at　the　graph　level.　CrowdGraph　comprises　three　main　components:　a　dynamic　graph　convolutional　backbone,　a　multi-scale　dilated　graph　convolution　module,　and　a　regression　head.　To　the　best　of　our　knowledge,　CrowdGraph　is　the　first　method　that　is　completely　formulated　based　on　the　Graph　Neural　Network　(GNN)　for　the　crowd　counting　task.　Extensive　experiments　demonstrate　that　the　proposed　CrowdGraph　outperforms　pure　CNN-based　and　pure　Transformer-based　weakly　supervised　methods　comprehensively　and　achieves　highly　competitive　counting　performance.