Title: Beyond Node-Centric Modeling: Sketching Signed Networks with Simplicial Complexes
Venue: NeurIPS 2025
Accepted: yes
Keywords: Signed Networks

Edge Embedding

Simplicial Complexes

Locality Sensitive Hashing

Link Sign Prediction
Abstract: Signed networks can reflect more complex connections through positive and negative edges, and cost-effective signed network sketching can significantly benefit an important link sign prediction task in the era of big data. Existing signed network embedding algorithms mainly learn node representation in the Graph Neural Network (GNN) framework with the balance theory. However, the node-wise representation learning methods either limit the representational power because they primarily rely on node pairwise relationship in the network, or suffer from severe efficiency issues. Recent research has explored simplicial complexes to capture higher-order interactions and integrated them into GNN frameworks. Motivated by that, we propose  EdgeSketch+,  a simple and effective edge embedding algorithm beyond traditional node-centric modeling that directly represents edges as low-dimensional vectors without transitioning from node embeddings. The proposed approach maintains a good balance between accuracy and efficiency by exploiting the Locality Sensitive Hashing (LSH) technique to swiftly capture the higher-order information derived from the simplicial complex in a manner of no learning processes. Experiments show that EdgeSketch+ matches state-of-the-art accuracy while significantly reducing runtime, achieving speedups of up to $546.07\times$ compared to GNN-based methods.

Beyond Node-Centric Modeling: Sketching Signed Networks with Simplicial Complexes

Abstract

Signed networks can reflect more complex connections through positive and negative edges, and cost-effective signed network sketching can significantly benefit an important link sign prediction task in the era of big data. Existing signed network embedding algorithms mainly learn node representation in the Graph Neural Network (GNN) framework with the balance theory. However, the node-wise representation learning methods either limit the representational power because they primarily rely on node pairwise relationship in the network, or suffer from severe efficiency issues. Recent research has explored simplicial complexes to capture higher-order interactions and integrated them into GNN frameworks. Motivated by that, we propose EdgeSketch+, a simple and effective edge embedding algorithm beyond traditional node-centric modeling that directly represents edges as low-dimensional vectors without transitioning from node embeddings. The proposed approach maintains a good balance between accuracy and efficiency by exploiting the Locality Sensitive Hashing (LSH) technique to swiftly capture the higher-order information derived from the simplicial complex in a manner of no learning processes. Experiments show that EdgeSketch+ matches state-of-theart accuracy while significantly reducing runtime, achieving speedups of up to 546 . 07 × compared to GNN-based methods 2 .

1 Introduction

Signed networks provide a richer and more nuanced description of the connections than unsigned networks by differentiating positive and negative relationships. This enables critical applications like analyzing social cohesion through friend/enemy ties and improving recommender systems via positive/negative feedback. Consequently, a significant task is link sign prediction , which aims to predict the sign of the edge in a signed network.

Current signed network embedding methods primarily use node-wise representation learning in Graph Neural Networks (GNNs) [1-9], incorporating the balance theory (i.e., a friend of my friends is my friend; a enemy of my enemies is my friend) [10, 11]. Although the node-wise signed network embedding approaches have achieved great success thanks to the balance theory, they either are trapped into the special circumstances of the edges such as isomorphic proximity or incur severe

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efficiency issues from neural network training. For example, in Figure 1, the balance theory cannot identify the edge ( 𝑣 11 , 𝑣 66 ) but ( 𝑣 1 , 𝑣 6 ) (i.e., enemies). Meanwhile, unsigned network embedding methods, including both node embedding [12-18] and edge embedding [19-23], lack mechanisms to capture signed network properties, resulting in performance degradation in dealing with signed networks.

Furthermore, a great many graph embedding methods concentrate on the node-wise interaction relationship, but real-world networks are increasingly complicated, rendering node-to-node relationships incompetent to delineate higher-order interaction relationships in the networks. For example, social network analysis emphasizes friend circles rather than individual pairs [24]. As shown in Figure 1, edges ( 𝑣 1 , 𝑣 6 ) and ( 𝑣 11 , 𝑣 66 ) , which are isomorphic at the node level, differ in higher-order structures -( 𝑣 11 , 𝑣 66 ) lies in two triangles while ( 𝑣 1 , 𝑣 6 ) is in only one. Recent advancements in GNNs have incorporated simplicial complexes [25-35], which exhibit the powerful representation capability by capturing the complicated, higher-order substructures in the network, yet these approaches demand huge computational cost due to massive parameter training.

By contrast, randomization offers high efficiency in algorithm design by avoiding laborious learning processes. A well-established framework, Locality-Sensitive Hashing (LSH) [36], supported by a solid theoretical foundation, embeds similar data objects into the same location in a low-dimensional space with higher probability than dissimilar ones by randomized hash functions, preserving their relative distances. To our knowledge, most graph hashing methods [37-51] efficiently represent graphs or nodes in reduced dimensions in a non-learning fashion, though [47, 51] enhance graph classification by converting node-based graphs into simplicial complex-based ones. As this integration remains underexplored, we focus on advancing signed network sketching using LSH and simplicial complexes, aiming for robust theoretical guarantees and strong experimental results.

In this paper, we seek to strike a good balance between accuracy and efficiency of sketching the signed network by leveraging the LSH technique to capture higher-order interaction relationship from the simplicial complex. We first propose a novel edge structure distance measure tailored for the edge surrounding, which is represented via the convolution operation on the edge itself (i.e., 1-simplex) and all its direct neighborhoods containing the adjacent edges (i.e., 1-simplexes), nodes (i.e., 0-simplexes) and triangles (i.e., 2-simplexes) under the simplicial complex, preserving intricate structural information beyond traditional node-wise neighborhoods (See Appendix C). Subsequently, our cost-effective algorithm beyond node-centric modeling, EdgeSketch+, iteratively sketches edge surroundings into low-dimensional binary vectors without learning, while maintaining low computational overhead. Additionally, by doubling the binary representation length, EdgeSketch+ enables integration with linear models, effectively addressing SVM kernel storage issues in large-scale scenarios. In summary, the main contributions of our work are as follows:

To our knowledge, this is the first work introducing simplicial complexes to signed networks, significantly improving embedding efficiency and link sign prediction.

We present a novel signed network sketching model called EdgeSketch+, which effectively and efficiently represents each signed edge by adopting the LSH technique to capture the higher-order interaction relationship from the simplicial complex.

The experimental results show that our proposed EdgeSketch+ algorithm enjoys good performance comparable to the state-of-the-art competitors, while remarkably reducing computational expenses, for example, running up to 546 . 07 × faster than the GNN-based signed network embedding methods.

2 Related Works

Graph Embedding with Simplicial Complex. In order to cope with the complicated interaction in the graph, the researchers have explored the use of simplicial complex in the GNN framework for applications such as trajectory prediction [34, 29], visual inspection [31] and missing data imputation [33]. MPSN [35] and SaNN [25] perform graph classification in the message passing mechanism with the simplicial complex, while N 𝑘 F [32] outperforms the existing message passing neural networks [52-54] by adopting differential 𝑘 -forms on simplicial complexes to augment geometric interpretability. SGAT [26] implements node embedding by injecting simplicial complexes into heterogeneous message passing networks, and SCRaW1 [27] learns node representation by random walks on higher-order simplicies. Furthermore, SCN [30] and EdgeRWSE [28] extend node (i.e., 0-simplex) embedding to edge (i.e., 1-simplex) embedding on message passing and random walks, respectively; the latter additionally preserves information from local/global positions of the nodes and local/global structures in the network based on edge-level random walks.

Signed Network Embedding. The signed networks assign the edges with a richer and more nuanced description such that the positive and negative edges are distinguished. In particular, link sign prediction is an important task unique to the signed networks and thus has attracted much attention from researchers. To our knowledge, most of the existing signed network embedding algorithms [1-4, 7, 5, 6, 55, 8] are node-wise representation under the balance theory [10, 11]. They usually apply a certain GNN method (e.g., GCN [53], GAT [52], GCL [56], etc.) to the signed networks based on the balance theory. RSGNN [1] aims to improve the robustness of the signed GNN methods by reducing the affect of noisy edges. Furthermore, GS-GNN [2], generalizing the balance theory to the 𝑘 -group theory [57, 58], describes the node relationship with all the groups as the global information and models the positive and negative edges as the local information. SGA [59] addresses graph sparsity and unbalanced triangle issues through structure-aware edge manipulation and curriculum learning. In addition, many node embedding algorithms [12-17] are initially used for the classic downstream tasks in the traditional networks, e.g., node classification and link prediction. By contrast, only a few works [19-23] focus on the edge embedding. These methods commonly lack the mechanism of capturing the unique properties of signed networks. Whether for signed networks or traditional networks, the above network embedding algorithms mainly rely on random walks, matrix factorization and graph neural networks, which pose challenges in terms of efficiency.

Graph Hashing. Although the GNN framework has achieved great progress, it suffers from huge computational resource and memory storage due to dramatic parameter training. To this end, the researchers have resorted to the Locality Sensitive Hashing (LSH) technique, which can efficiently conduct the high-dimensional data similarity estimation by exploiting a family of random hash functions to map the similar data objects to the close and even the same data points represented as vectors, e.g., MinHash [60], SimHash [61], etc. Some graph hashing algorithms [37-42, 62, 48-50] aim to represent each node in the network as a compact hashcode. The core idea of the methods is to extract and sketch the subtrees rooted at each node as the corresponding node representation. Although the methods can further derive the edge representation, they cannot preserve the complete characteristics of the edges. By contrast, others [43-47, 51] represent the whole graph as a lowdimensional feature vector for graph classification. Particularly, the works [47, 51] improve the performance of the above LSH-based graph classification by extracting the simplicial complex and then converting the original node-based graphs into multi-dimensional simplex-based graphs. Unfortunately, the whole graph hashing methods are not applicable in our problem setting.

3 Preliminaries

3.1 Edge-based Signed Network Embedding

Given a signed network 𝑔 = (V , E + ∪ E - ) , where V is a node set, E + ⊆ V × V is a positive edge set and E ⊆ V × V is a negative edge set. Note that E + ∩ E -= ∅ means that an edge 𝑒 is either positive or negative. Edge-based signed network embedding seeks to sketch each edge 𝑒 ∈ E + ∪ E as a low-dimensional vector x 𝑒 ∈ R 𝐷 , which facilitates the link sign prediction task.

3.2 Simplicial Complex

Given a network 𝑔 = (V , E) , where V is a node set and E is an edge set, we call it 𝑘 -simplex S 𝑘 = [ 𝑣 0 , 𝑣 1 , . . . , 𝑣 𝑘 ] if a node subset { 𝑣 0 , 𝑣 1 , . . . , 𝑣 𝑘 } ⊆ V forms a clique in 𝑔 , for example, a 0-simplex is a node, a 1-simplex is an edge, a 2-simplex is a triangle, etc. Also, given a 𝑘 -simplex S 𝑘 = [ 𝑣 0 , . . . , 𝑣 𝑘 ] and a ( 𝑘 -1 ) -simplex S 𝑘 -1 = [ 𝑣 0 , . . . , 𝑣 𝑘 ′ -1 , 𝑣 𝑘 ′ + 1 , . . . , 𝑣 𝑘 ] , S 𝑘 is a co-boundary adjacency and S 𝑘 -1 is a boundary adjacency. Formally, the boundary operator is 𝜕 S 𝑘 = ˝ 𝑘 𝑘 ′ = 0 (-1 ) 𝑘 ′ S 𝑘 -1 = ˝ 𝑘 𝑘 ′ = 0 (-1 ) 𝑘 ′ [ 𝑣 0 , . . . , 𝑣 𝑘 ′ -1 , 𝑣 𝑘 ′ + 1 , . . . , 𝑣 𝑘 ] . Furthermore, a simplicial complex C is a collection of different dimensional simplexes in the network. We let 𝐾 be the largest dimension of any simplex in C , and C 𝑘 be the subset composed of all the 𝑘 -simplexes. We encode the adjacency relationship between ( 𝑘 -1 ) -simplexes and 𝑘 -simplexes as +-, where the columns represent the corresponding coefficients of the ( 𝑘 -1 ) -simplexes in the above-mentioned boundary operator of all the 𝑘 -simplexes. Consequently, the Hodge Laplacian of a 𝐾 -dimensional simplicial complex is defined as

where each row and each column of L 𝑘 denotes a 𝑘 -simplex, 𝑘 ∈ { 0 , 1 , . . . , 𝐾 } . Similarly, L 𝑘 describes the adjacency relationship between any two 𝑘 -simplexes. Note that an entry of B 𝑘 / L 𝑘 is 0 if the two corresponding simplexes are not adjacent; otherwise, it is non-zero. The simplicial complex bridges geometric topology with algebraic topology, which further establishes a connection between graph analysis and topology. In a word, the simplicial complex assists in preserving higher-order interaction relationship in the network.

3.3 Locality Sensitive Hashing

An efficient framework for data sketching and similarity preserving backed by solid theoretical foundation is Locality-Sensitive Hashing (LSH) [36], which sketches the similar data objects as the same hashcode with a higher probability than the dissimilar ones by a family of randomized hash functions.

Definition 1 (Locality Sensitive Hashing). A family H of randomized functions is called ( 𝑑 1 , 𝑑 2 , 𝑝 1 , 𝑝 2 ) -sensitive if for two data objects p and q , and ∀ ℎ ∈ H :

If 𝑑𝑖𝑠𝑡 ( p , q ) ≤ 𝑑 1 , then Pr ( ℎ ( p ) = ℎ ( q )) ≥ 𝑝 1 ;

If 𝑑𝑖𝑠𝑡 ( p , q ) ≥ 𝑑 2 , then Pr ( ℎ ( p ) = ℎ ( q )) ≤ 𝑝 2 ,

where 𝑑 1 < 𝑑 2 under a certain distance measure 𝑑𝑖𝑠𝑡 and 0 ≤ 𝑝 2 < 𝑝 1 ≤ 1 .

4 Signed Network Sketching

In this section, we propose a novel signed network sketching model called EdgeSketch+ beyond node-centric modeling, which employs the simplicial complex to capture the higher-order interaction relationship in the signed network and then efficiently implements edge embedding in the LSH framework. The relevant definitions are provided in Appendix C.

4.1 The EdgeSketch+ Algorithm

We outline the proposed EdgeSketch+ method in Algorithm 1. The input consists of a signed network 𝑔 = (V , E + ∪ E - ) , the number of dimensions for edge embedding 𝐷 , the number of iterations 𝑅 , and 𝑅 sets of 3 × 𝐷 random vectors { h ( 𝑑,𝑟 ) 𝑘 } 2 ,𝐷,𝑅 𝑘 = 0 ,𝑑 = 1 ,𝑟 = 1 corresponding to 0-, 1- and 2-simplexes, respectively. The output returns each edge embedding at the 𝑅 th iteration as the final edge embedding.

First, Algorithm 1 extracts all the simplexes (Line 1), and establishes the simplex-based adjacency matrices and the 1-dimensional Hodge Laplacian (Lines 2-3). Subsequently, Algorithm 1 proceeds in an iterative way: we represent all the extracted simplexes by the convolution operation on the simplex-based adjacency matrices and the 1-dimensional Hodge Laplacian (Lines 5-7); further, we

Algorithm 1 The EdgeSketch+ Algorithm

aggregate (e.g., sum) each edge surrounding in the LSH framework such that the direct neighborhood information is effectively and efficiently perserved (Lines 8-11). Accordingly, the resulting edge embeddings are from the last iteration. We illustrate the overall procedure of the proposed EdgeSketch+ approach in Figure 4 of Appendix B.

The random vectors are produced off-line only once and shared globally. Therefore, there is no demand for high-performance hardware such as GPUs. As a consequence, the proposed EdgeSketch+ algorithm remarkably saves computation cost.

4.2 Theoretical Analysis

In this section, we theoretically analyze the proposed EdgeSketch+ algorithm.

4.2.1 EdgeSketch+ belongs to an LSH family

Proposition 1 . Let 𝑒 𝑖 and 𝑒 𝑗 be two edges, respectively, and 𝐷 be the size of their sketchings. Algorithm 1 is ( 𝑑 1 , 𝑑 2 , 1 -𝑑 1 𝐷 , 1 -𝑑 2 𝐷 ) -sensitive for some large 𝐷 :

if 𝑑𝑖𝑠𝑡 ( 𝑒 𝑖 , 𝑒 𝑗 ) ≥ 𝑑 2 , then Pr ( ℎ ( 𝑒 𝑖 ) = ℎ ( 𝑒 𝑗 )) ≤ 1 -𝑑 2 𝐷 ,

where 0 ≤ 𝑑 1 < 𝑑 2 ≤ 1 .

Proof . The Hamming distance between two edge sketchings varies from 0 to some large 𝐷 in the case of 𝐷 -dimensional sketches, i.e., 𝑑𝑖𝑠𝑡 ( 𝑒 𝑖 , 𝑒 𝑗 ) ∈ [ 0 , 𝐷 ] . Furthermore, we can convert the distance into the probability that they share the same hashcode, i.e.,

4.2.2 EdgeSketch+ produces a Hash Kernel

Definition 2 (Hash Kernel). Given any two edges, 𝑒 𝑖 and 𝑒 𝑗 , Algorithm 1 generates the 𝐷 -dimensional edge sketching under 𝑅 iterations, x ( 𝑅 ) 𝑒 𝑖 and x ( 𝑅 ) 𝑒 𝑗 , respectively. We define the hash kernel, 𝜅 𝐸𝑑𝑔𝑒𝑆𝑘𝑒𝑡𝑐ℎ + , between 𝑒 𝑖 and 𝑒 𝑗 as

Theorem 1 . The hash kernel matrix K ∈ R 𝑁 × 𝑁 , as defined in Eq. (3), is positive definite.

Proof . We rewrite Eq. (3) as

The value 𝜅 𝐸𝑑𝑔𝑒𝑆𝑘𝑒𝑡𝑐ℎ + ( 𝑒 𝑖 , 𝑒 𝑗 ) can be interpreted as the inner product of two vectors, each having a dimensionality of 2 𝐷 . As a result, K can be expressed as K = MM ⊤ , where M ∈ R 𝑁 × 2 𝐷 .

4.2.3 Concentration

We define the theoretical similarity between two edges, 𝑒 𝑖 and 𝑒 𝑗 , as

Then, we can show the highly-concentrated estimator of 𝑆𝑖𝑚 ( 𝑒 𝑖 , 𝑒 𝑗 ) .

Theorem 2 . Given two edges 𝑒 𝑖 and 𝑒 𝑗 , Algorithm 1 generates the corresponding 𝐷 -dimensional edge sketching x ( 𝑅 ) 𝑒 𝑖 and x ( 𝑅 ) 𝑒 𝑗 under 𝑅 iterations. ∀ 𝜖 > 0 , the probability of deviation between the kernel 𝜅 𝐸𝑑𝑔𝑒𝑆𝑘𝑒𝑡𝑐ℎ + ( 𝑒 𝑖 , 𝑒 𝑗 ) and their real similarity 𝑆𝑖𝑚 ( 𝑒 𝑖 , 𝑒 𝑗 ) is bounded by the following inequality

Proof . Let 𝑋 ( 𝑅 ) 𝑑 be a Bernoulli random variable, which takes 𝑋 ( 𝑅 ) 𝑑 = 1 with the probability of 𝑆𝑖𝑚 ( 𝑒 𝑖 , 𝑒 𝑗 ) . Furthermore, considering 𝐷 i.i.d Bernoulli random variables, 𝑋 ( 𝑅 ) 1 , 𝑋 ( 𝑅 ) 2 , · · · , 𝑋 ( 𝑅 ) 𝐷 , we have 𝑋 ( 𝑅 ) = 1 𝐷 ˝ 𝐷 𝑑 = 1 𝑋 ( 𝑅 ) 𝑑 and E [ 𝑋 ( 𝑅 ) ] = 𝑆𝑖𝑚 ( 𝑒 𝑖 , 𝑒 𝑗 ) . Consequently, we can directly use the Hoeffding bound [63] on the mean of the variables,

4.2.4 Time Complexity

Let 𝜈 be the average degree of the network, 𝑅 be the number of iterations, and 𝐷 be the number of dimensions for edge embedding. We denote the number of 0-simplexes, 1-simplexes and 2-simplexes as 𝑁 0 , 𝑁 1 and 𝑁 2 , respectively. In the real scenarios, the networks are commonly sparse, which means that O( 𝑁 0 ) = O( 𝑁 1 ) = O( 𝑁 2 ) [64, 65]. Consequently, it takes O( 𝑁 0 ) , O( 𝑁 1 ) and O( 𝜈𝑁 1 ) to extract 0-simplexes, 1-simplexes and 2-simplexes, respectively. Next, Algorithm 1 spends O( 2 𝑁 1 ) , O( 3 𝑁 2 ) and O( 𝑁 2 1 ) in generating the sparse matrices, B 1 , B 2 and L 1 , respectively. In the outer for loop, it requires O( 𝑁 1 𝐷 ) (Line 5), O( 𝑁 1 𝐷 ) (Line 6), O( 𝑁 1 𝐷 ) (Line 7) and O( 𝜈𝑁 1 𝐷 2 ) (Lines 8-11). Consequently, the total time complexity is O( 𝑁 2 1 + 𝜈𝑁 1 𝐷 2 𝑅 ) .

Additionally, we analyze the algorithm's representational power and space complexity in Appendix D.1 and Appendix D.2, respectively.

4.3 Incorporation with Linear Learning Models

As in Eq. (3), the 𝐷 -dimensional edge embedding derives nonlinear kernels. However, the explicit kernel computation is definitely accompanied with a precomputed Gram matrix with the size being the square of the number of training instances. Fortunately, the inner product in Eq. (4) indicates that the vectorized representation can be mapped into an inner product space by negation and concatenation, which remarkably speedups the training and testing processes without compromise to classification accuracy by the linear learning models such as logistic regression. Consequently, this approach

effectively addresses the issue of SVM training for large-scale problems, particularly when the training dataset exceeds available memory capacity. Taking the edge embedding vector [ 1 , 0 , 1 , 0 , 0 ] as an example, Eq. (4) transforms it into a 10-dimensional feature vector, i.e., 1 √ 5 ×[ 1 , 0 , 1 , 0 , 0 , 0 , 1 , 0 , 1 , 1 ] , where the underlined entries negate the corresponding bits in the original vector. Consequently, the concatenated vectors can be utilized by linear solvers, eliminating the need to store large-scale kernel matrices.

5 Experimental Results

In this section, we conduct the extensive experiments to evaluate the performance of the proposed EdgeSketch+ method. All dataset information is provided in Table 6 of Appendix E, where "BTC𝛼 " stands for Bitcoin-alpha, "BTC-O" for Bitcoin-OTC, "Slash." for Slashdot, and "Epin." for Epinions. For details on baselines and experimental settings, please refer to Appendix F.

5.1 Link Sign Prediction Results

OOM/OOT mean that the methods run out of memory/time.

Table 1 shows the experimental results. Clearly, our proposed EdgeSketch+ algorithm competes very well with all the competitors tailored for signed network embedding (i.e., RSGNN, SDGNN, SNEA, SGCL, SiGAT and GSGNN+SGA) - it performs best in most cases, with an average AUC lag of only 0.0253, compared to the top-performing baselines, which reflects that the proposed EdgeSketch+ algorithm has achieved strong precision and recall for the minority class but struggles to maintain balanced performance across all the thresholds due to the influence of the majority class; also, it runs fastest on all the datasets, achieving up to 546 . 07 × speedup 3 . Particularly, we would like to note that some GNN-based algorithms suffer from huge time or memory consumption on the two largest datasets, i.e., Slash. and Epin. 4 This reveals that the idea of integrating the simplicial complex into the LSH framework is very promising in efficiently recognizing the confusing patterns of signed networks with no data distribution fitting and further benefiting the quality of edge-based signed network embedding - our proposed EdgeSketch+ algorithm judges the edges more accurately with the aid of simplicial complexes. In the Appendix G, we analyze the experimental results about the edge embedding algorithms (i.e., EdgeRWSE, TER and edge2vec) and the LSH-based method (i.e., MPSketch) in more details.

5.2 Ablation Study

The proposed EdgeSketch+ algorithm preserves higher-order interaction relationship from the simplicial complex in the signed network by the LSH technique. Therefore, we conduct the ablation analysis w.r.t. the simplicies (i.e., 0-simplex, 1-simplex and 2-simplex) involved in the proposed EdgeSketch+ algorithm. To this end, we have the following variants,

EdgeSketch+L 1 only utilizes the Hodge Laplacian L 1 by removing the boundary adjacency matrix involving 0-simplexes, B 1 , and the co-boundary adjacency matrix involving 2simplexes, B 2 , simultaneously;

EdgeSketch+I ⊤ I replaces L 1 of EdgeSketch+L 1 with I ⊤ I , which operates the incidence matrix I in Eq. (1). Any entry of I 1 is 1 if the node is incident upon the edge; it is 0, otherwise.

We adopt the same parameters for the two variants for a fair comparison. Table 2 reports the experimental results in terms of 𝐷 = 300 and 𝑅 = 1 . EdgeSketch+I ⊤ I , with no simplicies involved, performs worst, which indicates that the simplicial complex, as the higher-order abstraction, definitely facilitates signed network sketching by preserving the higher-order interaction relationship. Furthermore, we observe the wider gap between EdgeSketch+ and EdgeSketch+L 1 , which implies that the 0-simplexes and 2simplexes involved in the edge surrounding are indispensable and the higher-order direct neighborhood information remarkably benefits the final edge embedding. Overall, the performance shows an obvious increasing trend as more simplicies are captured, i.e., from just incidence information to the edge surrounding.

5.3 Scalability

As shown in Section 4.2.4, the time and space complexities of the proposed EdgeSketch+ algorithm are both polynomial w.r.t. the number of edges, and thus we verify its scalability w.r.t. the the network property on a set of random, million-scale networks (i.e., |V| = 10 6 ) generated by the ErdosRenyi model [66]. Figure 2 reports the results in terms of memory consumption and runtime in case of 𝑅 = 2 and 𝐷 = 300 . The embedding time of EdgeSketch+ displays the slow quadratic growth, owing to 𝜈, 𝐷, 𝐷 2 , 𝑅 ≪ 𝑁 1 , and the memory consumption is empirically linear thanks to

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5.4 Hyper-parameter Sensitivity

The proposed EdgeSketch+ algorithm has two parameters, i.e., the embedding dimension 𝐷 and the number of iterations 𝑅 . We explore how these two parameters affect link sign prediction performance

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in effectiveness and runtime. We exhibit the results in terms of binary-f1 and end-to-end runtime on BTC𝛼 and Slash. in Figure 3.

It is evident that the accuracy performance is closely influenced by the embedding dimension 𝐷 and the number of iterations 𝑅 . Generally speaking, accuracy exhibits fluctuations as 𝐷 and 𝑅 increase on BTC𝛼 ; whereas we observe a clear convex surface on Slash. Particularly, EdgeSketch+ performs best in the case of the largest 𝐷 = 300 and the smallest 𝑅 = 1 . This result highlights two key insights: a larger 𝐷 captures more meaningful information and the simplicial complex, as a higher-order abstraction, retains sufficient information with a small 𝑅 . Conversely, increasing 𝑅 excessively would cause feature diffusion across simplexes, leading to the loss of local features. The end-to-end runtime demonstrates an empirical linear relationship with 𝑅 , even though the classification time is considered. This implies that the embedding process theoretically linear to 𝑅 dominates the whole link sign prediction task, and further EdgeSketch+ generates high-quality embedding vectors that are easy to classify.

5.5 Effectiveness of Edge Structure Distance

We define the edge structure distance based on the Hamming distance to describe the similarity between the edges of the signed networks in Appendix C. In order to verify its effectiveness, we compare with the common Cosine distance. Specifically, we precompute the Cosine kernel based on cosine distance and then feed it into SVM.

Table 3 shows the comparison results on BTC𝛼 and BTC-O. Clearly, our proposed edge similarity based on the Hamming distance is superior to the Cosine similarity in the LSH family.

5.6 Necessity of Edge Embedding

In order to demonstrate that the edge embedding as feature design definitely fosters the good performance, we directly feed the initialized edge vectors into the logistic regression classifier.

Table 4 shows the comparison results on BTC𝛼 and BTC-O. In the scenario where the same classifier model is adopted, our proposed edge embedding model performs much better than the naive method based on the only initialized edge vectors, which shows that the edge embedding effectively captures the higher-order structure information. By contrast, the naive method cap-

tures only the information of the edges themselves. Particularly, the AUC values of 0.5000 from the

naive method imply that good feature design is very necessary; otherwise, the classifier performs no better than random guessing.

6 Conclusion

In this paper, we propose a simple and very speedy signed network sketching model dubbed EdgeSketch+ beyond node-centric modeling, which can capture the higher-order information derived from the simplicial complex with more representational capability than the traditional node-to-node interactive relationship. The approach adopts the LSH technique to avoid substantial parameter training, while preserving as much structural information as possible. Furthermore, we provide the theoretical guarantees for the accuracy and the complexity of the edge representation. We conduct the extensive experiments of EdgeSketch+ and a collection of state-of-the-art methods. We evaluate its performance in terms of accuracy and runtime on a number of signed network datasets. The experimental results show that our proposed EdgeSketch+ method achieves the competitive accuracy performance with dramatically reduced runtime, which makes it more practical in the era of big data.

Acknowledgments

This work was supported by Open Project of Xiangjiang Laboratory (No.25XJ03020), National Natural Science Foundation of China (No. 62302528, 62522201, 62172449, 62202025, 72374070), Hunan Provincial Natural Science Foundation of China (2022JJ3021,2025JJ20071), Beijing Natural Science Foundation (No. L241050), Young Elite Scientist Sponsorship Program by CAST (No. YESS20230566), CCF-Huawei Populus Grove Fund CCF-Huawei (No. CCF-HuaweiFM2024005), High Performance Computing Center of Central South University, and Fundamental Research Fund Project of Beihang University.

References

Z. Zhang, J. Liu, X. Zheng, Y. Wang, P. Han, Y. Wang, K. Zhao, and Z. Zhang, 'RSGNN: A Model-agnostic Approach for Enhancing the Robustness of Signed Graph Neural Networks,' in WWW , 2023, pp. 60-70.

H. Liu, Z. Zhang, P. Cui, Y. Zhang, Q. Cui, J. Liu, and W. Zhu, 'Signed Graph Neural Network with Latent Groups,' in KDD , 2021, pp. 1066-1075.

J. Huang, H. Shen, L. Hou, and X. Cheng, 'SDGNN: Learning Node Representation for Signed Directed Networks,' in AAAI , no. 1, 2021, pp. 196-203.

Y. Li, Y. Tian, J. Zhang, and Y. Chang, 'Learning Signed Network Embedding via Graph Attention,' in AAAI , no. 04, 2020, pp. 4772-4779.

T. Derr, Y. Ma, and J. Tang, 'Signed Graph Convolutional Networks,' in ICDM , 2018, pp. 929-934.

L. Shu, E. Du, Y. Chang, C. Chen, Z. Zheng, X. Xing, and S. Shen, 'SGCL: Contrastive Representation Learning for Signed Graphs,' in CIKM , 2021, pp. 1671-1680.

J. Huang, H. Shen, L. Hou, and X. Cheng, 'Signed Graph Attention Networks,' in ICANN , 2019, pp. 566-577.

H. Sun, P. Tian, Y. Xiong, Y. Zhang, Y. Xiang, X. Jia, and H. Wang, 'DynamiSE: Dynamic Signed Network Embedding for Link Prediction,' Machine Learning , pp. 1-17, 2024.

L. Li, J. Liu, X. Ji, M. Wang, and Z. Zhang, 'Self-Explainable Graph Transformer for Link Sign Prediction,' in AAAI , 2025, pp. 12 084-12 092.

D. Cartwright and F. Harary, 'Structural Balance: A Generalization of Heider's Theory.' Psychological Review , vol. 63, no. 5, p. 277, 1956.

F. Heider, 'Attitudes and Cognitive Organization,' The Journal of Psychology , vol. 21, no. 1, pp. 107-112, 1946.

B. Perozzi, R. Al-Rfou, and S. Skiena, 'DeepWalk: Online Learning of Social Representations,' in KDD , 2014, pp. 701-710.

A. Grover and J. Leskovec, 'node2vec: Scalable Feature Learning for Networks,' in KDD , 2016, pp. 855-864.

J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, 'LINE: Large-scale Information Network Embedding,' in WWW , 2015, pp. 1067-1077.

C. Huang, M. Li, F. Cao, H. Fujita, Z. Li, and X. Wu, 'Are Graph Convolutional Networks with Random Weights Feasible?' IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 3, pp. 2751-2768, 2022.

Y. Yan, B. Jing, L. Liu, R. Wang, J. Li, T. Abdelzaher, and H. Tong, 'Reconciling Competing Sampling Strategies of Network Embedding,' in NeurIPS , 2023, pp. 6844-6861.

Y. Yan, Y. Hu, Q. Zhou, L. Liu, Z. Zeng, Y. Chen, M. Pan, H. Chen, M. Das, and H. Tong, 'PACER: Network Embedding from Positional to Structural,' in WWW , 2024, pp. 2485-2496.

G. Yang, M. Li, H. Feng, and X. Zhuang, 'Deeper Insights into Ddeep Graph Convolutional Networks: Stability and Generalization,' IEEE Transactions on Pattern Analysis and Machine Intelligence , 2025.

C. Wang, C. Wang, Z. Wang, X. Ye, and P. S. Yu, 'Edge2vec: Edge-based Social Network Embedding,' ACM Transactions on Knowledge Discovery from Data , vol. 14, no. 4, pp. 1-24, 2020.

H. Wang, R. Yang, K. Huang, and X. Xiao, 'Efficient and Effective Edge-wise Graph Representation Learning,' in KDD , 2023, pp. 2326-2336.

P. Bielak, T. Kajdanowicz, and N. V. Chawla, 'AttrE2vec: Unsupervised Attributed Edge Representation Learning,' Information Sciences , vol. 592, pp. 82-96, 2022.

J. Kim, T. Kim, S. Kim, and C. D. Yoo, 'Edge-labeling Graph Neural Network for few-shot Learning,' in CVPR , 2019, pp. 11-20.

L. Gong and Q. Cheng, 'Exploiting Edge Features for Graph Neural Networks,' in CVPR , 2019, pp. 9211-9219.

A. C. Wilkerson, T. J. Moore, A. Swami, and H. Krim, 'Simplifying The Homology of Networks via Strong Collapses,' in ICASSP , 2013, pp. 5258-5262.

S. Gurugubelli and S. P. Chepuri, 'SaNN: Simple Yet Powerful Simplicial-aware Neural Networks,' in ICLR , 2024.

S. H. Lee, F. Ji, and W. P. Tay, 'SGAT: Simplicial Graph Attention Network,' in IJCAI , 2022, pp. 3192-3200.

F. Frantzen and M. T. Schaub, 'Learning from Simplicial Data ased on Random Walks and 1d Convolutions,' in ICLR , 2024.

C. Zhou, X. Wang, and M. Zhang, 'Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes,' in NeurIPS , 2023, pp. 16 172-16 206.

C. Liu, D. Ruhe, F. Eijkelboom, and P. Forré, 'Clifford Group Equivariant Simplicial Message Passing Networks,' in ICLR , 2024.

H. Wu, A. Yip, J. Long, J. Zhang, and M. K. Ng, 'Simplicial Complex Neural Networks,' IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 46, no. 1, pp. 561-575, 2024.

M. Hajij, G. Zamzmi, T. Papamarkou, V. Maroulas, and X. Cai, 'Simplicial Complex Representation Learning,' in ICLR , 2021.

K. Maggs, C. Hacker, and B. Rieck, 'Simplicial Representation Learning with Neural 𝑘 -Forms,' in ICLR , 2024.

M. Yang, E. Isufi, and G. Leus, 'Simplicial Convolutional Neural Networks,' in ICASSP , 2022, pp. 8847-8851.

T. M. Roddenberry, N. Glaze, and S. Segarra, 'Principled Simplicial Neural Networks for Trajectory Prediction,' in ICML , 2021, pp. 9020-9029.

C. Bodnar, F. Frasca, Y. Wang, N. Otter, G. F. Montufar, P. Lio, and M. Bronstein, 'Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks,' in ICML , 2021, pp. 1026-1037.

P. Indyk and R. Motwani, 'Approximate Nearest Neighbors: towards Removing the Curse of Dimensionality,' in STOC , 1998, pp. 604-613.

W. Wu, B. Li, L. Chen, and C. Zhang, 'Efficient Attributed Network Embedding via Recursive Randomized Hashing,' in IJCAI-18 , 2018, pp. 2861-2867.

W. Wu, B. Li, C. Luo, and W. Nejdl, 'Hashing-Accelerated Graph Neural Networks for Link Prediction,' in WWW , 2021, pp. 2910-2920.

W. Wu, B. Li, C. Luo, W. Nejdl, and X. Tan, 'MPSketch: Message Passing Networks via Randomized Hashing for Efficient Attributed Network Embedding,' IEEE Transactions on Cybernetics , vol. 54, no. 5, pp. 2941-2954, 2024.

D. Bera, R. Pratap, B. D. Verma, B. Sen, and T. Chakraborty, 'QUINT: Node Embedding using Network Hashing,' IEEE Transactions on Knowledge and Data Engineering , vol. 35, no. 3, pp. 2987-3000, 2023.

D. Yang, B. Qu, J. Yang, L. Wang, and P. Cudre-Mauroux, 'Streaming Graph Embeddings via Incremental Neighborhood Sketching,' IEEE Transactions on Knowledge and Data Engineering , vol. 35, no. 5, pp. 5296-5310, 2023.

A. Celikkanat, F. D. Malliaros, and A. N. Papadopoulos, 'NODESIG: Binary Node Embeddings via Random Walk Diffusion,' in ASONAM , 2022, pp. 68-75.

B. Li, X. Zhu, L. Chi, and C. Zhang, 'Nested Subtree Hash Kernels for Large-scale Graph Classification over Streams,' in ICDM , 2012, pp. 399-408.

W. Wu, B. Li, L. Chen, X. Zhu, and C. Zhang, ' 𝐾 -Ary Tree Hashing for Fast Graph Classification,' IEEE Transactions on Knowledge and Data Engineering , vol. 30, no. 5, pp. 936-949, 2018.

C. Morris, N. M. Kriege, K. Kersting, and P. Mutzel, 'Faster Kernels for Graphs with Continuous Attributes via Hashing,' in ICDM , 2016, pp. 1095-1100.

M. Neumann, R. Garnett, C. Bauckhage, and K. Kersting, 'Propagation Kernels: Efficient Graph Kernels from Propagated Information,' Machine Learning , vol. 102, pp. 209-245, 2016.

X. Tan, W. Wu, and C. Luo, 'SCHash: Speedy Simplicial Complex Neural Networks via Randomized Hashing,' in SIGIR , 2023, pp. 1609-1618.

C. Aggarwal, G. He, and P. Zhao, 'Edge Classification in Networks,' in ICDE , 2016, pp. 1038-1049.

W. Wu, S. Li, C. Luo, and F. Li, 'Time- and Space-Efficiently Sketching Billion-Scale Attributed Networks,' IEEE Transactions on Knowledge and Data Engineering , vol. 37, no. 2, pp. 966-978, 2025.

W. Wu, S. Li, L. Chen, L. Fangfang, and C. Luo, 'Skeching Very Large-scale Dynamic Attributed Networks More Practically,' in WWW , 2025, pp. 5264-5274.

F. Li, H. Zhang, W. Li, and W. Wu, 'Heterogeneous Graph Embedding Made More Practical,' in SIGIR , 2025, pp. 688-697.

P. Veliˇ ckovi´ c, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, 'Graph Attention Networks,' in ICLR , 2018.

T. N. Kipf and M. Welling, 'Semi-Supervised Classification with Graph Convolutional Networks,' in ICLR , 2017.

K. Xu, W. Hu, J. Leskovec, and S. Jegelka, 'How Powerful are Graph Neural Networks?' in ICLR , 2019.

H. Liu, 'LightSGCN: Powering Signed Graph Convolution Network for Link Sign Prediction with Simplified Architecture Design,' in SIGIR , 2022, pp. 2680-2685.

Y. You, T. Chen, Y. Sui, T. Chen, Z. Wang, and Y. Shen, 'Graph Contrastive Learning with Augmentations,' NeurIPS , pp. 5812-5823, 2020.

M. Cucuringu, P. Davies, A. Glielmo, and H. Tyagi, 'SPONGE: A Generalized Eigenproblem for Clustering Signed Networks,' in AISTATS , 2019, pp. 1088-1098.

R.-C. Tzeng, B. Ordozgoiti, and A. Gionis, 'Discovering Conflicting Groups in Signed Networks,' NeurIPS , pp. 10 974-10 985, 2020.

Z. Zhang, L. Li, S. Wan, W. Wang, Z. Wang, Z. Lu, D. Hao, and W. Li, 'DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks,' in NeurIPS , 2024, pp. 117 041-117 069.

A. Z. Broder, M. Charikar, A. M. Frieze, and M. Mitzenmacher, 'Min-wise Independent Permutations,' in STOC , 1998, pp. 327-336.

M. S. Charikar, 'Similarity Estimation Techniques from Rounding Algorithms,' in STOC , 2002, pp. 380-388.

D. Yang, P. Rosso, B. Li, and P. Cudre-Mauroux, 'NodeSketch: Highly-Efficient Graph Embeddings via Recursive Sketching,' in KDD , 2019, pp. 1162-1172.

W. Hoeffding, 'Probability Inequalities for Sums of Bounded Random Variables,' in The Collected Works of Wassily Hoeffding , 1994, pp. 409-426.

J.-P. Eckmann and E. Moses, 'Curvature of Co-links Uncovers Hidden Thematic Layers in the World Wide Web,' Proceedings of the National Scademy of Dciences , vol. 99, no. 9, pp. 5825-5829, 2002.

Z. Zhang, P. Cui, H. Li, X. Wang, and W. Zhu, 'Billion-scale Network Embedding with Iterative Random Projection,' in ICDM , 2018, pp. 787-796.

P. ERDdS and A. R&wi, 'On Random Graphs I,' Publicationes Mathematicae , vol. 6, no. 18, pp. 290-297, 1959.

B. Weisfeiler and A. A. Lehman, 'A Reduction of a Graph to a Canonical Form and an Algebra Arising during this Reduction,' Nauchno-Technicheskaya Informatsia , vol. 2, no. 9, pp. 12-16, 1968.

N. Halko, P.-G. Martinsson, and J. A. Tropp, 'Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions,' SIAM Review , vol. 53, no. 2, pp. 217-288, 2011.

T. Lei, W. Jin, R. Barzilay, and T. Jaakkola, 'Deriving Neural Architectures from Sequence and Graph Kernels,' in ICML , 2017, pp. 2024-2033.

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Appendix

A Notation Table

The notations are summarized in Table 5.

B The EdgeSketch+ Algorithm

A Signed Network

extract

simplexes

build

matrices

Conv

0-simplex sketching

1-simplex sketching

2-simplex sketching

0/1/2-simplexes

AGG

sgn(·)

Sketching

L1

B1

B2

R

0-

1-

2-

+

-

+

+

+

-

-

-

C Definitions

Definition 3 (Edge Surrounding). The edge surrounding centers around the edge itself (i.e., 1simplex), with its direct neighborhood from the viewpoint of the simplicial complex, including the adjacent edges (i.e., 1-simplexes), nodes (i.e., 0-simplexes) and triangles (i.e., 2-simplexes). We can represent the edge surrounding via the convolution operation on the simplex-based adjacency matrices B 1 , B 2 and the 1-dimensional Hodge Laplacian L 1 , which effectively preserves the direct neighborhood information.

This definition extends the traditional neighborhood relationship in graph mining, which is limited to the same-level, node-centric adjacency relationship.

Definition 4 (Edge Sketching). The edge 𝑒 's sketching under 𝑅 iterations is a 𝐷 -dimensional binary vector, x ( 𝑅 ) 𝑒 = [ 𝑥 ( 𝑅 ) 𝑒, 1 , 𝑥 ( 𝑅 ) 𝑒, 2 , · · · , 𝑥 ( 𝑅 ) 𝑒,𝐷 ] , which is defined in terms of the edge surrounding projected into 𝑅 sets of 3 × 𝐷 random vectors in an iterative manner. The vector element 𝑥 ( 𝑅 ) 𝑒,𝑑 = 1 if the 𝑑 th projection is non-negative, and 0 otherwise.

The projection operation is actually a linear combination of three kinds of simplexes from the edge surrounding on three random vectors, which is mathematically equivalent to the edge surrounding holistically projected into a target random vector.

Definition 5 (Edge Structure Distance). Given any two edges, 𝑒 𝑖 and 𝑒 𝑗 , Algorithm 1 generates the 𝐷 -dimensional edge sketching under 𝑅 iterations, x ( 𝑅 ) 𝑒 𝑖 and x ( 𝑅 ) 𝑒 𝑗 , respectively. We define the edge structure distance as the Hamming distance between their sketchings, i.e.,

where 𝑥 ( 𝑅 ) 𝑒 𝑖 ,𝑑 ∈ { 0 , 1 } , and 1 ( 𝑠𝑡𝑎𝑡𝑒 ) = 1 , if 𝑠𝑡𝑎𝑡𝑒 is true, and 1 ( 𝑠𝑡𝑎𝑡𝑒 ) = 0 , otherwise.

The distance represents the number of the target random vectors that lie in the same direction with exactly one of two edge surroundings.

D Theoretical Analysis

D.1 Representational Power

The work [54] has demonstrated that the WL isomorphism test [67] has the maximal representational power to discriminate the substructures in the graph because the test ensures that different substructures is embedded into distinct locations in the feature space.

Motivated by the LSH framework, we can quantize the representational power of our proposed EdgeSketch+ algorithm from a probabilistic perspective. Assuming that ℏ ( 𝑟 ) denotes the 𝑟 -th sketching process of the proposed probabilistic model, we have the probabilistic expression as follows

where 𝑟 = 1 , 2 , · · · , 𝑅 . Formally, Eq. (9) illustrates that the proposed EdgeSketch+ model could represent two substructures in the graph as the same feature vector with the probability of their theoretical similarity.

D.2 Space complexity

EdgeSketch+ requires storing the simplex-based adjacency matrices (i.e., O( 2 𝑁 1 ) for B 1 and O( 3 𝑁 2 ) for B 2 ) and the Hodge Laplacian (i.e., O( 𝜈𝑁 1 ) for L 1 ) as well as the embedding for 0-simplexes (i.e., O( 𝑁 0 𝐷 ) ), 1-simplexes (i.e., O( 𝑁 1 𝐷 ) ) and 2-simplexes (i.e., O( 𝑁 2 𝐷 ) ); also, the random vectors need O( 𝐷 2 𝑅 ) . Therefore, the space complexity is practically O( 𝑁 1 𝐷 + 𝐷 2 𝑅 ) .

E Datasets

We implement the link sign prediction task on the following signed networks from the SNAP group 5 .

Bitcoin-alpha is a who-trust-whom Bitcoin-Alpha trading network, where the users give trust or distrust tags to others for the sake of security.

Bitcoin-OTC is a who-trust-whom Bitcoin-OTC trading network, where the users give trust or distrust tags to others for the sake of security.

Slashdot is a news website that covers technology, science and culture. The users are allowed to tag each other as friends or foes.

Epinions is a who-trust-whom online social network from the consumer review site. The users decide whether to trust each other.

Particularly, for Bitcoin-alpha and Bitcoin-OTC, users rate others negatively (i.e., {-10 , -9 , · · · , -2 , -1 } ) or positively (i.e., { 10 , 9 , · · · , 2 , 1 } ); we consider the negative scores

as the negative edges and the positive scores as the positive edges, as shown in [1, 3]. We initialize the node features with the positive in-degree, the negative in-degree, the positive out-degree and the negative out-degree; then, we initialize the features of high-dimensional simplexes (i.e., edges and triangles) by taking the sum of the node features. The above datasets are summarized in Table 6.

F Baselines and Experiment Setting

We compare the proposed EdgeSketch+ method with the following state-of-the-art approaches in the link sign prediction task 6 .

RSGNN [1] aims to denoise the signed network in the node representation learning process based on the balance theory.

SDGNN [3] learns node representation by aggregating messages from different signed, social relations in the signed network based on the balance theory.

SNEA [4] estimates the importance coefficients of node pairs via the self-attention mechanism when representing nodes in the signed network based on the balance theory.

SGCL [6] learns node representation by applying the graph contrastive learning to signed networks based on the balance theory.

SiGAT [7] learns node representation by applying the graph attention network to signed networks based on the balance theory.

GSGNN+SGA [2, 59] combines GS-GNN's [2] latent group modeling with SGA's [59] augmentation to address graph sparsity and unbalanced triangles.

EdgeRWSE [28] learns edge representation based on edge-level (i.e., 1-simplex) random walks with edge-level node position information supplemented.

TER [20] exploits the randomized singular value decomposition [68] of edge transition matrices to obtain edge representation.

edge2vec [19] utilizes deep autoencoders to capture local and global structure information of the embedded edges.

MPSketch [39] generates the node embeddings by using the LSH technique to pass messages in the MPNN framework [69].

All the compared methods have their codes provided by their respective authors, and we adopt the recommended hyperparameters in their papers for the learning methods. Also, we conduct the experiments with different values of 𝑅 ∈ { 1 , 2 , 3 , 4 , 5 } for MPSketch and EdgeSketch+. Finally, we set 𝑅 = 1 on Bitcoin-alpha and 𝑅 = 4 on Bitcoin-OTC for MPSketch, which runs out of memory on Slashdot and Epinions; we set 𝑅 = 1 on Bitcoin-alpha and 𝑅 = 1 on Bitcoin-OTC, 𝑅 = 1 on Slashdot and 𝑅 = 1 on Epinions for EdgeSketch+. We set the embedding dimension 𝐷 as 300 and the cutoff time of 24 hours. All the experiments are conducted on Linux with 2.90GHz 128 Intel Xeon Platinum 8375C CPU, 1.5T RAM and NVIDIA A10 GPU (24GB RAM). When the experiments encounter out-of-memory errors on the GPU, we would switch the computational device to the CPU. Following the previous works [1, 2], we achieve the signed network embedding and then conduct the link sign prediction task by the logistic regression classifier for the methods which are not trained in an end-to-end manner. Particularly, for our proposed EdgeSketch+ algorithm, we expand the embedding vectors and then feed them into the logistic regression classifier, as shown in Section

4.3; for MPSketch, we use the Hamming distance and SVM classifier recommended in the original paper 7 . Otherwise, we employ the end-to-end training. We report the mean of binary-f1, accuracy, auc, macro-f1 and end-to-end runtime in the 5-fold cross validation in Table 1. We provide the code in the attachment.

G Analysis of Experimental Results

The proposed EdgeSketch+ model outperforms all the edge embedding algorithms (i.e., EdgeRWSE, TER and edge2vec). In spite of the simplicial complex involved, EdgeSketch+ is unsurprisingly superior to EdgeRWSE, because EdgeRWSE focuses on random walks on simplicial complexes, with the edge patterns among the signed triangles neglected, that is to say, the neighbors that share 0simplexes is only considered; by contrast, EdgeSketch+ preserves such important information related to 2-simplexes to distinguish some difficult patterns under the balance theory. Besides, EdgeRWSE exhausts memory on Slashdot and Epinions, mainly due to the memory consumption required for walking between a large number of simplexes. TER, as an efficient and effective edge embedding method recently claimed by its authors, definitely performs more efficiently than the learning-based competitors by randomized singular value decomposition [68], but EdgeSketch+ goes towards more efficient and effective by leveraging the advantages of the LSH technique and the simplicial complex. Also, EdgeSketch+ clearly outperforms edge2vec in terms of effectiveness and efficiency, because edge2vec essentially preserves local and global proximities from the node-level perspective by deep autoencoders. This again illustrates that the simplicial complex is able to identify more patterns.

Also, we compare EdgeSketch+ against the most recent LSH-based node embedding algorithm, MPSketch, which derives edge embedding by concatenating two corresponding node embeddings. Evidently, MPSketch performs worst, and even runs out of memory on Slashdot and Epinions, because it cannot effectively capture the substructure features in the signed networks and more importantly, it embeds into the nonlinear Hamming space such that the precomputed kernel matrix is too big to fit in memory, which makes it difficult to scale. Although the two methods do not need learning, EdgeSketch+ still performs faster than MPSketch by two orders of magnitude. The main reasons are as follows: 1) MPSketch sketches a large, aggregated information pool, and even iterates more than EdgeSketch+ on Bitcoin-OTC capture sufficient information; 2) The SVM classifier training based on the Hamming kernel is time-consuming. This again implies that MPSketch has the limited capabilities of capturing the patterns in the signed networks.

In addition, we would like to note that all the competitors implicitly/explicitly represent the nodes or edges as 32/64-bit embedding vectors, while our proposed EdgeSketch+ algorithm only generates the binary vectors with nearly no sacrificing the result quality, which means that EdgeSketch+ is able to enjoy lower memory footprints than the compared methods in signed network embedding. Overall, our proposed EdgeSketch+ model achieves an excellent balance between accuracy and efficiency.

H Limitations

This work has two main limitations. First, although EdgeSketch+ operates in an unsupervised manner without using edge signs during sketching, it relies on downstream classifiers that require labeled data. This setup may limit its applicability in domains where labeled signed edges are scarce. Second, the current approach leverages simplicial complexes constructed from triangle structures, which may miss relevant higher-order interactions not captured by 2-simplices. Extending the framework to incorporate higher-dimensional simplices could provide more comprehensive modeling, albeit at the cost of increased computational complexity.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel and well-motivated edge-based signed network sketching method, which preserves higher-order relationship from simplicial complexes beyond the traditional node-centric modeling in the signed network. The method is elegant in its simplicity yet powerful in its expressiveness, marking a significant departure from traditional node-based embedding techniques. To my best knowledge, very few existing works have explored the use of higher-order topological structures in graphs, particularly for signed networks. The authors skillfully bridge the gap between traditional signed graph sketching and higher-order topology, demonstrating that capturing structures beyond pairwise interactions significantly enhances downstream performance, avoiding the issue from the balance theory. Furthermore, the Locality-Sensitive Hashing (LSH) framework helps to efficiently capture higher-order interactions without the computational burden of traditional GNNs. Particularly, integration with linear learning models guarantees real-world large-scale applications. Consequently, the method effectively balances performance and efficiency while maintaining strong representational capability. The work is both theoretically grounded and practically impactful, offering a scalable and versatile tool for signed network analysis.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The paper is well-written and easy to follow.\n2. The idea of simplicial complexes as higher-order abstraction in signed network embedding is interesting. The method beyond the traditional node-centric scheme provides a new insight into signed network embedding. \n3. Extensive experimental results across multiple datasets and baselines highlight the effectiveness, efficiency, scalability, and domain-agnostic nature of the proposed framework.\n4. The paper provides solid theoretical analysis, including LSH-based theory, error bound, and complexity, with the performance guaranteed. Particularly, incorporation with linear models benefits large network application, avoiding the issue of SVM training, and also the experiments verify this point. \n\nWeaknesses:\n\n1. It would be better if the paper provides a notation table to clarify many symbols.\n2. The authors empirically verify the necessity of edge embedding. The authors are encouraged to further demonstrate that the method effectively preserves the essential structural information of the data.  \n3. Section 4.2.3 theoritically analyzes the error bound. Empirical study is encouraged.\n\nQUESTIONS:\nsee the weaknesses\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-16"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nGraph neural networks are a popular modeling paradigm for graph structured data, and have enjoyed popularity in both research and industrial settings for exactly this purpose. One common application of GNNs is the construction of node-embeddings that can be used in downstream applications through transfer learning. While popular, this approach traditionally suffers from two different problems -- poor computational scaling and limited expressivity. This is usually done by computing embeddings, and is usually done in a self-supervised fashion. It is well known at this point that using equivariant GNNs to do this at a node-level leads to the isomorphic node problem. For signed graphs, many methods further depend on balance theory which, when violated, can lead to poor results. The authors situate their work in this area of the literature, and present EdgeSketch+. This method presents a computationally efficient technique for generating edge-embeddings, with a strong focus specifically on signed graphs. Efficiency is realized through the use of locality sensitive hashing, which is done in a way that captures higher-order simplical structures. The authors explore the theoretical aspects of their work and contribute multiple interesting bounds, and then present experiments on link sign prediction intended to justify their method.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. The work is well motivated and addresses a significant problem in the space.\n2. The work is well written and presented. There are no significant typos, and is relatively easy to follow despite the mathematically intricate nature of the work.\n3. Figure 1 provides a fantastic intuition for the problem that sets the tone for the rest of the work\n4. 4.3, the incorporation into linear learning models is just _cool_.\n5. Runtime performance validates that this model just works and is just fast. 546x speedup is incredible.\n6. The hyperparameter sensitivity is clearly experimented and explored, and the method looks fairly robust\n7. The theoretical results are clearly outlined and succinct. In particular, the concentration bound is particularly clear.\n8. The ablation studies clearly demonstrate the importance of each component\n\n**Weaknesses**\n1. The experiments also lack standard deviations\n2. Only 4 datasets\n3. Limited to 2-simplicies without a path forward towards expanding to higher-order tasks\n4. The application is quite limited in that it's focused only on signed networks with strong triangular structures.\n5. Missing baselines, although I'm happy to give them a pass given that the code isn't publicly available.\n\nQUESTIONS:\n1. How would you extend this to higher-order simplical complexes?\n2. Are there any modifications that you'd make to the method to make it inductive? It seems like none would be required, but I wanted to make sure.\n3. How would you make the model more expressive?\n4. Would a more advanced decoder yield better results?\n5. Could this be applied to non-signed link prediction?\n6. How does computational performance change as edge density increases?\n7. How sensitive is the method to the choice of distance metric in the LSH family?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper points out the limitations of node-centric graph embedding methods for link sign prediction, and proposes to overcome the limitations by introducing higher-order interactions in the form of simplicial complexes. It proposes a learning-free method EdgeSketch+, which computes edge embeddings iteratively by performing locality-sensitive hashing on newly defined edge surroundings. Theoretical analysis is provided to characterize the key properties of EdgeSketch+. Experimental results on four public datasets validate the effectiveness of EdgeSketch+. Extensive ablation studies help to understand the characteristics and behaviors of EdgeSketch+, e.g., the scalability, and the sensitivity to the hyperparameters.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\nS1. This paper is clearly and concisely written. It is easy to follow and understand.\n\nS2. It is novel to explore simplicial complexes for link sign prediction. The choice of LSH achieves a good balance between accuracy and efficiency, which makes EdgeSketch+ practical for large-scale real-world datasets.\n\nS3. The theoretical analysis characterizes the key properties of EdgeSketch+, which help to understand the behavior of the algorithm.\n\nS4. The experimental design is comprehensive, and the results are significant in most cases. Specifically, the results on the two large datasets, Slash. and Epin., validate the superior performance of EdgeSketch and its computational efficiency.\n\n**Weaknesses**\n\nCurrently, I am not aware of any major weaknesses in the paper.\n\nQUESTIONS:\nQ1. To my understanding, triangles play special roles in link sign prediction. There are multiple possible configurations of a triangle, which present different real-world meanings and have different impacts on link sign prediction. Is it possible to relate your methods to such observations? For example, you may build a connection with the traditional theory of signed networks, provide statistics on different configurations of triangles, or provide a case study on how these triangles improve the accuracy of link prediction.\n\nQ2. Can you discuss how EdgeSketch+ can deal with dynamic signed networks? For example, how to update the simplex embeddings and which are relevant? What is the time complexity and space complexity?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces EdgeSketch+, a fast and simple signed network sketching algorithm that leverages simplicial complexes and Locality Sensitive Hashing (LSH) to efficiently generate edge embeddings for signed graphs. Instead of learning node embeddings and deriving edge scores, it directly embeds edges by exploiting higher-order simplicial structures (0-, 1-, and 2-simplexes) to capture rich local geometry, while maintaining scalability through randomized hashing. Experimental results on standard signed network datasets demonstrate competitive prediction performance with up to 546× lower runtime than GNN-based baselines, with ablation studies highlighting the importance of simplicial components.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\nThe paper is well-written and easy to understand. EdgeSketch+ is simple yet elegant — combining higher-order simplicial structures with locality-sensitive hashing to capture rich interactions without expensive training. Its ability to use 0-, 1-, and 2-simplexes efficiently while preserving key properties is compelling, and the theoretical guarantees strengthen the contribution. The experiments are thorough and well-designed, showing competitive or better accuracy than strong baselines with much lower runtime. The ablation studies convincingly show the importance of each simplicial component and highlight the scalability and practicality of the approach.\n\n### Weaknesses\nWhile EdgeSketch+ is the first to use simplicial complexes for signed networks, the overall architecture presented has been around for a while.\n\nIn Algorithm 1, lines 5 and 6 refer to convolution operators `ConvB1` and `ConvB2`. The two arrays make it look like $L_1$ and $L_2$ are used for convolution. However, the text says they use adjacency matrices $B_1$, $B_2$. Shouldn’t these be better referred to as $ConvL_0$ and $ConvL_2$ in the context of Hodge Laplacians, for consistency? Please clarify notation and what $L_0$, $L_1$, $L_2$ actually mean in this step. Also, the dimension of embeddings after each step is not clear and would be helpful to include them to for ease of reading.\n\nSince the signed edges can be treated edge orientation in a simplicial complex, it would strengthen the empirical evidence if EdgeSketch+ is compared with simplicial representation learning methods like SaNN, SGAT, etc.\n\nQUESTIONS:\n- Clarify the notation and terminology in Algorithm 1.\n- State explicitly the dimensionality of the embedding after each iteration and after aggregation.\n- The seed is chosen and is not random. Was it fixed arbitrarily or cherry picked? How sensitive are the results to this choice? Is fixing a single seed standard practice in this line of work? Some discussion of randomness/stability would help. \n- The largest signed dataset used (Epinions) is still small by modern standards. Scalability is a known issue with such higher-order methods. Can the authors comment on whether EdgeSketch+ applies to unsigned graphs, and how it might scale and perform on much larger datasets like those in OGB?\n\nLIMITATIONS:\nAuthors have addressed the limitations.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}