Towards a Class-Aware Information Granulation for Graph Embedding and Classification

Baldini, Luca; Martino, Alessio; Rizzi, Antonello

doi:10.1007/978-3-030-70594-7_11

Cited by 6 publications

(3 citation statements)

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“…Granular Computing Approach for Labelled Graphs (GRALG) is a classification system suitable for dealing with (fully) labelled graphs grounded on the Granular Computing paradigm [30,31]. GRALG was originally proposed in [32] and later improved in [33][34][35][36][37][38], addressing some computational drawbacks in the original implementation. As anticipated, GRALG follows the Granular Computing paradigm, hence it aims at automatically extracting pivotal mathematical entities known in the technical literature as information granules, able to characterize the data at hand as much as possible.…”

Section: The Gralg Classification Systemmentioning

confidence: 99%

Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis

et al. 2022

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Network traffic analysis, and specifically anomaly and attack detection, call for sophisticated tools relying on a large number of features. Mathematical modeling is extremely difficult, given the ample variety of traffic patterns and the subtle and varied ways that malicious activity can be carried out in a network. We address this problem by exploiting data-driven modeling and computational intelligence techniques. Sequences of packets captured on the communication medium are considered, along with multi-label metadata. Graph-based modeling of the data are introduced, thus resorting to the powerful GRALG approach based on feature information granulation, identification of a representative alphabet, embedding and genetic optimization. The obtained classifier is evaluated both under accuracy and complexity for two different supervised problems and compared with state-of-the-art algorithms. We show that the proposed preprocessing strategy is able to describe higher level relations between data instances in the input domain, thus allowing the algorithms to suitably reconstruct the structure of the input domain itself. Furthermore, the considered Granular Computing approach is able to extract knowledge on multiple semantic levels, thus effectively describing anomalies as subgraphs-based symbols of the whole network graph, in a specific time interval. Interesting performances can thus be achieved in identifying network traffic patterns, in spite of the complexity of the considered traffic classes.

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Section: The Gralg Classification Systemmentioning

confidence: 99%

Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis

et al. 2022

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“…where g i ∈ K is the ith pattern of cluster K and g is the representative element of the cluster. Since the Granulator deals with non-geometric entities (i.e., graphs), we consider the medoid of the cluster as its representative [41]. Finally, the cardinality of the cluster is defined as the relative size of the cluster K with respect to the overall number of candidate information granules:…”

Section: Random Walkmentioning

confidence: 99%

“…The percentages are chosen according to the following rule: the larger the dataset, the higher the subsampling rate. For the sake of shorthand, we omit any sensitivity analysis on the behavior of the Granulators as a function of W. In this regard, we refer the interested reader to our previous works [41,46,66] The values for W for both clique-based and path-based Granulators are summarized in Table 2.…”

Section: Algorithmic Setupmentioning

confidence: 99%

On Information Granulation via Data Clustering for Granular Computing-Based Pattern Recognition: A Graph Embedding Case Study

2022

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Granular Computing is a powerful information processing paradigm, particularly useful for the synthesis of pattern recognition systems in structured domains (e.g., graphs or sequences). According to this paradigm, granules of information play the pivotal role of describing the underlying (possibly complex) process, starting from the available data. Under a pattern recognition viewpoint, granules of information can be exploited for the synthesis of semantically sound embedding spaces, where common supervised or unsupervised problems can be solved via standard machine learning algorithms. In this work, we show a comparison between different strategies for the automatic synthesis of information granules in the context of graph classification. These strategies mainly differ on the specific topology adopted for subgraphs considered as candidate information granules and the possibility of using or neglecting the ground-truth class labels in the granulation process. Computational results on 10 different open-access datasets show that by using a class-aware granulation, performances tend to improve (regardless of the information granules topology), counterbalanced by a possibly higher number of information granules.

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