Title: Future Link Prediction Without Memory or Aggregation
Venue: NeurIPS 2025
Accepted: yes
Keywords: temporal graph learning; future link prediction
Abstract: Future link prediction on temporal graphs is a fundamental task with wide applicability in real-world dynamic systems. These scenarios often involve both recurring (seen) and novel (unseen) interactions, requiring models to generalize effectively across both types of edges. However, existing methods typically rely on complex memory and aggregation modules, yet struggle to handle unseen edges. In this paper, we revisit the architecture of existing temporal graph models and identify two essential but overlooked modeling requirements for future link prediction: representing nodes with unique identifiers and performing target-aware matching between source and destination nodes. To this end, we propose Cross-Attention based Future Link Predictor on Temporal Graphs (CRAFT), a simple yet effective architecture that discards memory and aggregation modules and instead builds on two components: learnable node embeddings and cross-attention between the destination and the source's recent interactions. This design provides strong expressive power and enables target-aware modeling of the compatibility between candidate destinations and the source's interaction patterns. Extensive experiments on diverse datasets demonstrate that CRAFT consistently achieves superior performance with high efficiency, making it well-suited for large-scale real-world applications.


Peer reviews:
comments: SUMMARY:
This paper introduces CRAFT, which discards memory and aggregation modules for future link prediction on temporal graphs.

STRENGTHS AND WEAKNESSES:
Strength:
1. The paper is well-organized and the overall presentation is clear and coherent.
2. The proposed method achieves strong empirical results on the task considered in the paper.

Weakness:
1. The roles of the embedding and memory components appear to be quite similar in functionality, yet the paper does not clearly differentiate between the two.
2. The link prediction task formulation differs from conventional dynamic graph representation learning settings. It remains uncertain whether the proposed method outperforms existing models such as DyGFormer under a standardized task formulation.
3. In Section 4.3, the number of candidate destination nodes is controlled by a hyperparameter q. The method used to select destination nodes may significantly influence the final results. However, the paper lacks a sensitivity analysis on this hyperparameter.
4. Although the paper claims that the memory component does not help in handling unseen nodes, the embedding-based approach does not seem to offer any specific mechanism for such cases either. This raises the question of whether the two can, in fact, be treated equivalently.
5. The paper lacks a concrete case study to visually or intuitively validate its key contributions.

QUESTIONS:
Please see weakness

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-27

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents CRAFT (Cross-Attention based Future Link Predictor on Temporal Graphs), an architecture for future link prediction in temporal graphs. The authors identify two critical yet underexplored requirements for this task: incorporating unique node identifiers and enabling target-aware matching between source and destination nodes. Departing from prior approaches that rely on memory and aggregation modules, CRAFT adopts a simpler design composed of two key components: learnable node embeddings and a cross-attention mechanism that aligns candidate destination nodes with the source node’s recent interaction history. This architecture facilitates expressive and adaptive modeling of temporal interaction patterns while maintaining architectural simplicity, enabling effective generalization to both seen and unseen edges.\n\nSTRENGTHS AND WEAKNESSES:\nThis manuscript presents several strengths.\n\n(S1) The writing is clear and easy to follow.\n\n(S2) The proposed architecture is easy to implement and suitable for unseen future links.\n\n(S3) The baselines and datasets are comprehensive.\n\nNevertheless, this manuscript also has several weaknesses that should be addressed.\n\n(W1) The motivation for unique node identifiers is relatively weak. In real applications of temporal graphs, different nodes should have rich and different features, why these features cannot be used to differentiate nodes? \n\n(W2) I think it is a bit wired that the framework only utilizes learned node embeddings and positional encoding, without utilizing any node features and edge features. Although I noticed that the authors claim many of the benchmark’s datasets lack node features. However, this is not the case in practical scenarios. As the example in your Figure 3, the products ‘iphone/air pods/iphone case’ should have texts, images, and other statistical features if we want to conduct a real next-item- recommendation task. If we assume nodes have no features, how could we know what does a specific node (say node 100) indicate? And how could such a system be useful in practice? Hence, I think the practical utility of the proposed model is rather limited.\n\n(W3) Moreover, how does the model handle newly coming nodes? Since practical temporal graphs will involve not only unseen edges but also unseen nodes as time evolving. The node embeddings are learned during model training. How does the model handle these unseen nodes seems to be a crucial limitation.\n\n\n(W3) The paper claims that the learnable node embedding will enable powerful expressiveness. However, there lacks a theoretical justification that the embedding could improve expressiveness for the temporal prediction task. Theoretical results of static gnns may not be seamlessly transformed into temporal gnns.\n\nQUESTIONS:\nNone\n\nLIMITATIONS:\nNone", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-28"}

comments: SUMMARY:
This paper introduces CRAFT architecture, whose most significant differences from the previous models are (1) the introduction of unique node identifiers and (2) the use of cross attention mechanism (coined as target-aware matching in the paper).
The one of the strongest motivations of this paper is, the memory or aggregation components commonly used in previous work may not be required, if trainable a trainable node embedding unique for each node is used instead.
Given a source node and a set of candidate destinations, it first finds k neighbors for the source node (in the embedding space), sort them by recency, and add trainable positional encodings to node embeddings. Then it applies several rounds of cross attention mechanism, adds elapsed time information, and then finally predicts the score for each candidate destination.
From experiments, it is empirically proven that the suggested CRAFT method outperforms former approaches, while at the same time achieving reduced computational complexity.

STRENGTHS AND WEAKNESSES:
Strengths
* Large performance improvement introduced by simple change
* Paper is organized well and easy to understand
* Analyses are done thoroughly

Weaknesses
* Some details are not clear: e.g. the formulation of TimeProjection function (I assume it's similar to JODIE's though)
* I believe it can't handle when a *node* is not present during training, i.e. inductive setup, which is the scenario often discussed in literature.

QUESTIONS:
* Is there a way to obtain node embeddings for unseen nodes? I believe the use of node attributes & aggregation in previous work is mainly for this.

LIMITATIONS:
Clarification & discussion on setups where there exist unseen node and unseen edge would be preferred.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces a novel method to work on link prediction in dynamic graphs, featuring two components: learnable node embeddings and a \"target-aware matching\", relying on a cross-attention mechanism.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper clearly exposes its aim, and the solution they bring to existing litterature. However, I am afraid that there is a deep problem plaguing the whole paper: authors deal with a problem which is not (exactly) the same as the one presented in the existing litterature. They perform and improve link prediction _for a given node_, while other methods analyse a whole graph and propose to predict links _at the graph level_, which is very different. \nSecond problem (related to the first one): while authors mention in 4.2.2 (and Fig3) the use case of a recommender system scenario, they completely neglect this litterature, while it would clearly be more relevant to their problem, since recommendation focuses on a _candidate user_ with a potential set of items. There are already a large body of works on recommending items in a dynamic setting, with or without graph learning (and with several attempts to encode time as presented here, see Sequential Recommendation¹). But TGN, TGAT and others (JODIE, DyRep) were not meant to work by focusing on a user/node as it is done here. \n\n¹: note that in a recommender system, the candidate set usually is the entire collection of items\n\nIn light of this, all the paper is affected: the Related Work section is far too short or out of scope (1st paragraph), Experiments confront CRAFT to irrelevant methods (or omit relevant baselines), etc.\n\nAlso, in 4.2.1, it is claimed that GNNs are plagued by \"lack of distinctive node embeddings\", sustained by a 2020 paper. The 5-year gap is important here, as most GNNs have been using random/other initial embeddings for years.\n\nMinor remarks.\n\nBibliography: \n- \"Attention is all you need\" as more than one author…\n- use {} to prevent capital letters from being transformed to lowercase (eg, gnns)\n- BPR was published, don't cite arxiv version when possible\n\nQUESTIONS:\n- the sampling of negative items is a subject in itself in the Recommender System community. It is shortly/vaguely described here (l262-263). What does \"We adopt the negative samples from the benchmarks\" mean? Is the random selection focusing on a node, or is it global?\n- there are imprecisions in the formalism: the graph model looks like a \"link stream\" model. Is it exactly one, or not? Also, \"since both pairs share two common neighbors\" (l178): in a graph, nodes usually have neighbors, not edges.\n- \"repeat time\" is an important concept in your work, however not defined. What does it mean? What happens to \"repeat time\" if an edge appears irregularly?\n- at the core of the model, there is the hyperparameter k, the size of a candidate set. The value of k is not evaluated, however it should have a tremendous impact on the CRAFT's performance. It seems with the sentence l181 that [4] and [36] deal with experiments on this, but it should probably be expanded. Could you explain what effect you expect k to have?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}