diff --git a/pymc/model_graph.py b/pymc/model_graph.py
index 93f8c3a9af..00ebd4aa6c 100644
--- a/pymc/model_graph.py
+++ b/pymc/model_graph.py
@@ -21,17 +21,37 @@
 from aesara.graph import Apply
 from aesara.graph.basic import ancestors, walk
 from aesara.scalar.basic import Cast
+from aesara.tensor.basic import get_scalar_constant_value
 from aesara.tensor.elemwise import Elemwise
 from aesara.tensor.random.op import RandomVariable
 from aesara.tensor.var import TensorConstant, TensorVariable
 
 import pymc as pm
 
+from pymc.distributions import Discrete
+from pymc.distributions.discrete import DiracDelta
 from pymc.util import get_default_varnames, get_var_name
 
 VarName = NewType("VarName", str)
 
 
+def check_zip_graph_from_components(components):
+    """
+    This helper function checks if a mixture sub-graph corresponds to a
+    zero-inflated distribution using its components, a list of length two.
+    """
+    if not any(isinstance(var.owner.op, DiracDelta) for var in components):
+        return False
+
+    dirac_delta_idx = 1 - int(isinstance(components[0].owner.op, DiracDelta))
+    dirac_delta = components[dirac_delta_idx]
+    other_comp = components[1 - dirac_delta_idx]
+
+    return (get_scalar_constant_value(dirac_delta.owner.inputs[3]) == 0) and isinstance(
+        other_comp.owner.op, Discrete
+    )
+
+
 class ModelGraph:
     def __init__(self, model):
         self.model = model
@@ -154,17 +174,42 @@ def _make_node(self, var_name, graph, *, nx=False, cluster=False, formatting: st
             shape = "box"
             style = "rounded, filled"
             label = f"{var_name}\n~\nMutableData"
-        elif v.owner and isinstance(v.owner.op, RandomVariable):
+        elif v.owner and (v in self.model.basic_RVs):
             shape = "ellipse"
-            if hasattr(v.tag, "observations"):
+            if v in self.model.observed_RVs:
                 # observed RV
                 style = "filled"
             else:
-                shape = "ellipse"
                 style = None
-            symbol = v.owner.op.__class__.__name__
-            if symbol.endswith("RV"):
-                symbol = symbol[:-2]
+            symbol = v.owner.op._print_name[0]
+            if symbol == "MarginalMixture":
+                components = v.owner.inputs[2:]
+                if len(components) == 2:
+                    component_names = [var.owner.op._print_name[0] for var in components]
+                    if check_zip_graph_from_components(components):
+                        # ZeroInflated distribution
+                        component_names.remove("DiracDelta")
+                        symbol = f"ZeroInflated{component_names[0]}"
+                    else:
+                        # X-Y mixture
+                        symbol = f"{'-'.join(component_names)}Mixture"
+                elif len(components) == 1:
+                    # single component dispatch mixture
+                    symbol = f"{components[0].owner.op._print_name[0]}Mixture"
+                else:
+                    symbol = symbol[:-2]  # just MarginalMixture
+            elif symbol == "Censored":
+                censored_dist = v.owner.inputs[0]
+                symbol = symbol + censored_dist.owner.op._print_name[0]
+            elif symbol == "Truncated":
+                truncated_dist = v.owner.op.base_rv_op
+                symbol = symbol + truncated_dist._print_name[0]
+            elif symbol == "RandomWalk":
+                innovation_dist = v.owner.inputs[1].owner.op._print_name[0]
+                if innovation_dist == "Normal":
+                    symbol = "Gaussian" + symbol
+                else:
+                    symbol = innovation_dist + symbol
             label = f"{var_name}\n~\n{symbol}"
         else:
             shape = "box"
diff --git a/pymc/tests/test_model_graph.py b/pymc/tests/test_model_graph.py
index 1df3588465..91db13610f 100644
--- a/pymc/tests/test_model_graph.py
+++ b/pymc/tests/test_model_graph.py
@@ -22,6 +22,17 @@
 
 import pymc as pm
 
+from pymc.distributions import (
+    Cauchy,
+    Censored,
+    GaussianRandomWalk,
+    Mixture,
+    Normal,
+    RandomWalk,
+    StudentT,
+    Truncated,
+    ZeroInflatedPoisson,
+)
 from pymc.exceptions import ImputationWarning
 from pymc.model_graph import ModelGraph, model_to_graphviz, model_to_networkx
 from pymc.tests.helpers import SeededTest
@@ -360,3 +371,55 @@ def test_subgraph(self, var_names, vars_to_plot, compute_graph):
         mg = ModelGraph(model_with_different_descendants())
         assert set(mg.vars_to_plot(var_names=var_names)) == set(vars_to_plot)
         assert mg.make_compute_graph(var_names=var_names) == compute_graph
+
+
+@pytest.mark.parametrize(
+    "symbolic_dist, dist_kwargs, display_name",
+    [
+        (ZeroInflatedPoisson, {"psi": 0.5, "mu": 5}, "ZeroInflatedPoisson"),
+        (
+            Censored,
+            {"dist": Normal.dist(Normal.dist(0.0, 5.0), 2.0), "lower": -2, "upper": 2},
+            "CensoredNormal",
+        ),
+        (
+            Mixture,
+            {"w": [0.5, 0.5], "comp_dists": Normal.dist(0.0, 5.0, shape=(2,))},
+            "NormalMixture",
+        ),
+        (
+            Mixture,
+            {"w": [0.5, 0.5], "comp_dists": [Normal.dist(0.0, 5.0), StudentT.dist(7.0)]},
+            "Normal-StudentTMixture",
+        ),
+        (
+            Mixture,
+            {
+                "w": [0.3, 0.45, 0.25],
+                "comp_dists": [Normal.dist(0.0, 5.0), StudentT.dist(7.0), Cauchy.dist(1.0, 1.0)],
+            },
+            "MarginalMixture",
+        ),
+        (
+            GaussianRandomWalk,
+            {"init_dist": Normal.dist(0.0, 5.0), "steps": 10},
+            "GaussianRandomWalk",
+        ),
+        (Truncated, {"dist": StudentT.dist(7), "upper": 3.0}, "TruncatedStudentT"),
+        (
+            RandomWalk,
+            {
+                "innovation_dist": pm.StudentT.dist(7),
+                "init_dist": pm.Normal.dist(0, 1),
+                "steps": 10,
+            },
+            "StudentTRandomWalk",
+        ),
+    ],
+)
+def test_symbolic_distribution_display(symbolic_dist, dist_kwargs, display_name):
+    with pm.Model() as model:
+        symbolic_dist("x", **dist_kwargs)
+
+    graph = model_to_graphviz(model)
+    assert display_name in graph.source