diff --git a/src/coreml.js b/src/coreml.js
index 0c8c8ec5ca..17810f356a 100644
--- a/src/coreml.js
+++ b/src/coreml.js
@@ -165,7 +165,7 @@ coreml.Graph = class {
             predictedProbabilitiesName = predictedProbabilitiesName ? predictedProbabilitiesName : '?';
             let labelProbabilityInput = this._updateOutput(labelProbabilityLayerName, labelProbabilityLayerName + ':labelProbabilityLayerName');
             let operator = classifier.ClassLabels;
-            this._nodes.push(new coreml.Node(this._metadata, this._group, operator, null, classifier[operator], [ labelProbabilityInput ], [ predictedProbabilitiesName, predictedFeatureName ]));
+            this._nodes.push(new coreml.Node(this._metadata, this._group, operator, null, '', classifier[operator], [ labelProbabilityInput ], [ predictedProbabilitiesName, predictedFeatureName ]));
         }
     }
 
@@ -183,7 +183,7 @@ coreml.Graph = class {
             for (const p of preprocessing) {
                 let input = p.featureName ? p.featureName : preprocessorOutput;
                 preprocessorOutput = preprocessingInput + ':' + preprocessorIndex.toString();
-                this._createNode(scope, group, p.preprocessor, null, p[p.preprocessor], [ input ], [ preprocessorOutput ]);
+                this._createNode(scope, group, p.preprocessor, null, '', p[p.preprocessor], [ input ], [ preprocessorOutput ]);
                 preprocessorIndex++;
             }
             for (const node of inputNodes) {
@@ -200,10 +200,11 @@ coreml.Graph = class {
 
     _loadModel(model, scope, group) {
         this._groups = this._groups | (group.length > 0 ? true : false);
+        const description = model && model.description && model.description.metadata && model.description.metadata.shortDescription ? model.description.metadata.shortDescription : ''; 
         if (model.neuralNetworkClassifier) {
             const neuralNetworkClassifier = model.neuralNetworkClassifier;
             for (const layer of neuralNetworkClassifier.layers) {
-                this._createNode(scope, group, layer.layer, layer.name, layer[layer.layer], layer.input, layer.output);
+                this._createNode(scope, group, layer.layer, layer.name, description, layer[layer.layer], layer.input, layer.output);
             }
             this._updateClassifierOutput(group, neuralNetworkClassifier);
             this._updatePreprocessing(scope, group, neuralNetworkClassifier.preprocessing);
@@ -212,7 +213,7 @@ coreml.Graph = class {
         else if (model.neuralNetwork) {
             const neuralNetwork = model.neuralNetwork;
             for (const layer of neuralNetwork.layers) {
-                this._createNode(scope, group, layer.layer, layer.name, layer[layer.layer], layer.input, layer.output);
+                this._createNode(scope, group, layer.layer, layer.name, description, layer[layer.layer], layer.input, layer.output);
             }
             this._updatePreprocessing(scope, group, neuralNetwork.preprocessing);
             return 'Neural Network';
@@ -220,7 +221,7 @@ coreml.Graph = class {
         else if (model.neuralNetworkRegressor) {
             const neuralNetworkRegressor = model.neuralNetworkRegressor;
             for (const layer of neuralNetworkRegressor.layers) {
-                this._createNode(scope, group, layer.layer, layer.name, layer[layer.layer], layer.input, layer.output);
+                this._createNode(scope, group, layer.layer, layer.name, description, layer[layer.layer], layer.input, layer.output);
             }
             this._updatePreprocessing(scope, group, neuralNetworkRegressor);
             return 'Neural Network Regressor';
@@ -244,7 +245,7 @@ coreml.Graph = class {
             return 'Pipeline Regressor';
         }
         else if (model.glmClassifier) {
-            this._createNode(scope, group, 'glmClassifier', null, 
+            this._createNode(scope, group, 'glmClassifier', null, description,
                 {
                     classEncoding: model.glmClassifier.classEncoding, 
                     offset: model.glmClassifier.offset, 
@@ -256,39 +257,43 @@ coreml.Graph = class {
             return 'Generalized Linear Classifier';
         }
         else if (model.glmRegressor) {
-            this._createNode(scope, group, 'glmRegressor', null, 
+            this._createNode(scope, group, 'glmRegressor', null, description,
                 model.glmRegressor,
                 [ model.description.input[0].name ],
                 [ model.description.output[0].name ]);
             return 'Generalized Linear Regressor';
         }
         else if (model.dictVectorizer) {
-            this._createNode(scope, group, 'dictVectorizer', null, model.dictVectorizer,
+            this._createNode(scope, group, 'dictVectorizer', null, description,
+                model.dictVectorizer,
                 [ model.description.input[0].name ],
                 [ model.description.output[0].name ]);
             return 'Dictionary Vectorizer';
         }
         else if (model.featureVectorizer) {
-            this._createNode(scope, group, 'featureVectorizer', null, model.featureVectorizer, 
+            this._createNode(scope, group, 'featureVectorizer', null, description,
+                model.featureVectorizer, 
                 coreml.Graph._formatFeatureDescriptionList(model.description.input),
                 [ model.description.output[0].name ]);
             return 'Feature Vectorizer';
         }
         else if (model.treeEnsembleClassifier) {
-            this._createNode(scope, group, 'treeEnsembleClassifier', null, model.treeEnsembleClassifier.treeEnsemble, 
+            this._createNode(scope, group, 'treeEnsembleClassifier', null, description,
+                model.treeEnsembleClassifier.treeEnsemble, 
                 [ model.description.input[0].name ],
                 [ model.description.output[0].name ]);
             this._updateClassifierOutput(group, model.treeEnsembleClassifier);
             return 'Tree Ensemble Classifier';
         }
         else if (model.treeEnsembleRegressor) {
-            this._createNode(scope, group, 'treeEnsembleRegressor', null, model.treeEnsembleRegressor.treeEnsemble, 
+            this._createNode(scope, group, 'treeEnsembleRegressor', null, description,
+                model.treeEnsembleRegressor.treeEnsemble, 
                 [ model.description.input[0].name ],
                 [ model.description.output[0].name ]);
             return 'Tree Ensemble Regressor';
         }
         else if (model.supportVectorClassifier) {
-            this._createNode(scope, group, 'supportVectorClassifier', null, 
+            this._createNode(scope, group, 'supportVectorClassifier', null, description,
                 {   
                     coefficients: model.supportVectorClassifier.coefficients, 
                     denseSupportVectors: model.supportVectorClassifier.denseSupportVectors,
@@ -305,7 +310,7 @@ coreml.Graph = class {
             return 'Support Vector Classifier';
         }
         else if (model.supportVectorRegressor) {
-            this._createNode(scope, group, 'supportVectorRegressor', null, 
+            this._createNode(scope, group, 'supportVectorRegressor', null, description,
                 {
                     coefficients: model.supportVectorRegressor.coefficients, 
                     kernel: model.supportVectorRegressor.kernel,
@@ -317,7 +322,7 @@ coreml.Graph = class {
             return 'Support Vector Regressor';
         }
         else if (model.arrayFeatureExtractor) {
-            this._createNode(scope, group, 'arrayFeatureExtractor', null, 
+            this._createNode(scope, group, 'arrayFeatureExtractor', null, description,
                 { extractIndex: model.arrayFeatureExtractor.extractIndex },
                 [ model.description.input[0].name ],
                 [ model.description.output[0].name ]);
@@ -327,7 +332,7 @@ coreml.Graph = class {
             const categoryType = model.oneHotEncoder.CategoryType;
             const oneHotEncoderParams = { outputSparse: model.oneHotEncoder.outputSparse };
             oneHotEncoderParams[categoryType] = model.oneHotEncoder[categoryType];
-            this._createNode(scope, group, 'oneHotEncoder', null, 
+            this._createNode(scope, group, 'oneHotEncoder', null, description,
                 oneHotEncoderParams,
                 [ model.description.input[0].name ],
                 [ model.description.output[0].name ]);
@@ -339,7 +344,7 @@ coreml.Graph = class {
             let imputerParams = {};
             imputerParams[imputedValue] = model.imputer[imputedValue];
             imputerParams[replaceValue] = model.imputer[replaceValue];
-            this._createNode(scope, group, 'oneHotEncoder', null, 
+            this._createNode(scope, group, 'oneHotEncoder', null, description,
                 imputerParams,
                 [ model.description.input[0].name ],
                 [ model.description.output[0].name ]);
@@ -347,14 +352,14 @@ coreml.Graph = class {
             
         }
         else if (model.normalizer) {
-            this._createNode(scope, group, 'normalizer', null, 
+            this._createNode(scope, group, 'normalizer', null, description,
                 model.normalizer,
                 [ model.description.input[0].name ],
                 [ model.description.output[0].name ]);
             return 'Normalizer';
         }
         else if (model.wordTagger) {
-            this._createNode(scope, group, 'wordTagger', null, 
+            this._createNode(scope, group, 'wordTagger', null, description,
                 model.wordTagger,
                 [ model.description.input[0].name ],
                 [ 
@@ -366,7 +371,7 @@ coreml.Graph = class {
             return 'Word Tagger';
         }
         else if (model.textClassifier) {
-            this._createNode(scope, group, 'textClassifier', null, 
+            this._createNode(scope, group, 'textClassifier', null, description,
                 model.textClassifier,
                 [ model.description.input[0].name ],
                 [ model.description.output[0].name ]);
@@ -379,7 +384,7 @@ coreml.Graph = class {
                 iouThreshold: model.nonMaximumSuppression.iouThreshold, 
                 confidenceThreshold: model.nonMaximumSuppression.confidenceThreshold
             };
-            this._createNode(scope, group, 'nonMaximumSuppression', null, 
+            this._createNode(scope, group, 'nonMaximumSuppression', null, description,
                 nonMaximumSuppressionParams,
                 [
                     model.nonMaximumSuppression.confidenceInputFeatureName,
@@ -397,21 +402,21 @@ coreml.Graph = class {
             const visionFeaturePrintParams = {
                 scene: model.visionFeaturePrint.scene
             }
-            this._createNode(scope, group, 'visionFeaturePrint', null,
+            this._createNode(scope, group, 'visionFeaturePrint', null, description,
                 visionFeaturePrintParams,
                 [ model.description.input[0].name ],
                 [ model.description.output[0].name ]);
             return 'Vision Feature Print';
         }
         else if (model.soundAnalysisPreprocessing) {
-            this._createNode(scope, group, 'soundAnalysisPreprocessing', null,
+            this._createNode(scope, group, 'soundAnalysisPreprocessing', null, description,
                 model.soundAnalysisPreprocessing,
                 [ model.description.input[0].name ],
                 [ model.description.output[0].name ]);
             return 'Sound Analysis Preprocessing';
         }
         else if (model.kNearestNeighborsClassifier) {
-            this._createNode(scope, group, 'kNearestNeighborsClassifier', null,
+            this._createNode(scope, group, 'kNearestNeighborsClassifier', null, description,
                 model.kNearestNeighborsClassifier,
                 [ model.description.input[0].name ],
                 [ model.description.output[0].name ]);
@@ -419,18 +424,24 @@ coreml.Graph = class {
             return 'kNearestNeighborsClassifier';
         }
         else if (model.itemSimilarityRecommender) {
-            const itemSimilarityRecommenderParams = {
-                itemStringIds: model.itemSimilarityRecommender.itemStringIds.vector,
-                itemItemSimilarities: model.itemSimilarityRecommender.itemItemSimilarities
-            }
-            this._createNode(scope, group, 'itemSimilarityRecommender', null,
-                itemSimilarityRecommenderParams,
+            this._createNode(scope, group, 'itemSimilarityRecommender', null, description,
+                {
+                    itemStringIds: model.itemSimilarityRecommender.itemStringIds.vector,
+                    itemItemSimilarities: model.itemSimilarityRecommender.itemItemSimilarities
+                },
                 model.description.input.map((feature) => feature.name),
                 model.description.output.map((feature) => feature.name));
             return 'itemSimilarityRecommender'
         }
+        else if (model.linkedModel) {
+            this._createNode(scope, group, 'linkedModel', null, description,
+                model.linkedModel.linkedModelFile,
+                [ model.description.input[0].name ],
+                [ model.description.output[0].name ]);
+            return 'customModel';
+        }
         else if (model.customModel) {
-            this._createNode(scope, group, 'customModel', null,
+            this._createNode(scope, group, 'customModel', null, description,
                 { className: model.customModel.className, parameters: model.customModel.parameters },
                 [ model.description.input[0].name ],
                 [ model.description.output[0].name ]);
@@ -439,7 +450,7 @@ coreml.Graph = class {
         throw new coreml.Error("Unknown model type '" + JSON.stringify(Object.keys(model)) + "'.");
     }
 
-    _createNode(scope, group, operator, name, data, inputs, outputs) {
+    _createNode(scope, group, operator, name, description, data, inputs, outputs) {
         inputs = inputs.map((input) => scope[input] ? scope[input].argument : input);
         outputs = outputs.map((output) => {
             if (scope[output]) {
@@ -455,7 +466,7 @@ coreml.Graph = class {
             return output;
         });
 
-        const node = new coreml.Node(this._metadata, group, operator, name, data, inputs, outputs);
+        const node = new coreml.Node(this._metadata, group, operator, name, description, data, inputs, outputs);
         this._nodes.push(node);
         return node;
     }
@@ -583,13 +594,14 @@ coreml.Argument = class {
 
 coreml.Node = class {
 
-    constructor(metadata, group, operator, name, data, inputs, outputs) {
+    constructor(metadata, group, operator, name, description, data, inputs, outputs) {
         this._metadata = metadata;
         if (group) {
             this._group = group;
         }
         this._operator = operator;
         this._name = name || '';
+        this._description = description || '';
         this._attributes = [];
         let initializers = [];
         if (data) {
@@ -620,6 +632,10 @@ coreml.Node = class {
         return this._name;
     }
 
+    get description() {
+        return this._description;
+    }
+
     get metadata() {
         return this._metadata.type(this.operator);
     }
diff --git a/test/models.json b/test/models.json
index 488ee1eeee..42c1778f19 100644
--- a/test/models.json
+++ b/test/models.json
@@ -1459,6 +1459,13 @@
     "format": "Core ML v1",
     "link":   "https://github.com/gavi/Iris"
   },
+  {
+    "type":   "coreml",
+    "target": "LinkedUpdatableTinyDrawingClassifier.mlmodel",
+    "source": "https://github.com/lutzroeder/netron/files/4500539/LinkedUpdatableTinyDrawingClassifier.zip[LinkedUpdatableTinyDrawingClassifier.mlmodel]",
+    "format": "Core ML v4",
+    "link":   "https://github.com/lutzroeder/netron/issues/193"
+  },
   {
     "type":   "coreml",
     "target": "MessageClassifier.mlmodel",